├── CalcMetrics.m ├── ONMF_Multi_PGD_search.m ├── README.md ├── bestMap.m ├── digit ├── mfeat-fac.txt ├── mfeat-fou.txt ├── mfeat-kar.txt ├── mfeat-mor.txt ├── mfeat-pix.txt ├── mfeat-zer.txt ├── mfeat.info ├── mfeat.tar └── mfeat │ ├── mfeat-fac │ ├── mfeat-fou │ ├── mfeat-kar │ ├── mfeat-mor │ ├── mfeat-pix │ ├── mfeat-zer │ └── mfeat.info ├── hungarian.m ├── litekmeans.m ├── load_data.m ├── nmi.m ├── objective_ONMF_Multi.m └── run.m /CalcMetrics.m: -------------------------------------------------------------------------------- 1 | function [AC, nmi_value, error_cnt] = CalcMetrics(label, result) 2 | 3 | result = bestMap(label, result); 4 | error_cnt = sum(label ~= result); 5 | AC = length(find(label == result))/length(label); 6 | 7 | nmi_value = nmi(label, result); 8 | 9 | -------------------------------------------------------------------------------- /ONMF_Multi_PGD_search.m: -------------------------------------------------------------------------------- 1 | function [U_total,V, U_star_total, Loss,average_nmi,average_ac] = ONMF_Multi_PGD_search(X, W, option, block_size) 2 | num_passes = option.pass; 3 | num_views = numel(X); 4 | total = size(X{1}, 1); 5 | label = option.label; 6 | alpha = option.alpha; 7 | beta = option.beta; 8 | skip_loss = option.loss; 9 | maxIter = option.maxiter; 10 | 11 | U_star_total = cell(num_passes,1); 12 | k = option.k; 13 | tol = option.tol; 14 | num_feature = zeros(num_views,1); 15 | V = cell(num_views,1); 16 | for i = 1:num_views 17 | num_feature(i) = size(X{i},2); 18 | V{i} = rand(num_feature(i), k); 19 | end 20 | A = cell(num_views, 1); 21 | B = cell(num_views, 1); 22 | num_block = ceil(total/block_size); 23 | Loss = zeros(num_passes, num_block); 24 | 25 | average_nmi = zeros(num_passes, 2); 26 | average_ac = zeros(num_passes, 2); 27 | X_sofar = cell(num_views,1); 28 | 29 | for pass = 1:num_passes 30 | t1 = tic; 31 | if pass == 1 32 | U_total = cell(num_views,1); 33 | U_star_total{pass} = zeros(size(X{i},1),k); 34 | for i = 1:num_views 35 | U_total{i} = zeros(size(X{i},1),k); 36 | A{i} = zeros(size(V{i})); 37 | B{i} = zeros(size(V{i},2)); 38 | 39 | end 40 | else 41 | U_star_total{pass} = U_star_total{pass-1}; 42 | end 43 | for i = 1:num_views 44 | X_sofar{i} = []; 45 | end 46 | 47 | for block_index = 1:num_block 48 | data_range = (block_index-1)*block_size+1:block_index*block_size; 49 | U = cell(num_views,1); 50 | for i =1:num_views 51 | U{i} = rand(block_size, k); 52 | end 53 | U_star = rand(block_size,k); 54 | if block_index==num_block 55 | data_range = (block_index-1)*block_size+1:total; 56 | for i = 1:num_views 57 | U{i} = rand(total-(num_block-1)*block_size,k); 58 | end 59 | U_star = rand(total-(num_block-1)*block_size,k); 60 | end 61 | % Get data blocks, W blocks. 62 | X_block = cell(num_views,1); 63 | W_block = cell(num_views,1); 64 | for i = 1:num_views 65 | X_block{i} = X{i}(data_range,:); 66 | W_block{i} = W{i}(data_range, data_range); 67 | if(total > size(X_sofar{i},1)) 68 | X_sofar{i} = [X_sofar{i}; X_block{i}]; 69 | end 70 | end 71 | % Get initial U and V; 72 | if pass == 1 73 | if(block_index==1) 74 | % alternating initialize U and V 75 | for j = 1:num_views 76 | U{j} = rand(size(U{j})); 77 | V{j} = rand(size(V{j})); 78 | end 79 | else 80 | % Only initialize U 81 | WW = W_block; 82 | for j = 1:num_views 83 | WW{i} = W_block{i}'*W_block{i}; 84 | U{j} = rand(size(U{j})); 85 | end 86 | end 87 | % Initialize U* 88 | CU = alpha(1)*(W_block{1}.^2)*U{1}; 89 | CC = alpha(1)*(W_block{1}.^2); 90 | for i = 2:num_views 91 | CU = CU + alpha(i)*(W_block{i}.^2)*U{i}; 92 | CC = CC + alpha(i)*(W_block{i}.^2); 93 | end 94 | CC_inv = diag(1./diag(CC)); 95 | U_star = CC_inv*CU; 96 | else 97 | % Only use previous U and U_star 98 | for i = 1:num_views 99 | U{i} = U_total{i}(data_range,:); 100 | end 101 | U_star = U_star_total{pass}(data_range,:); 102 | end 103 | 104 | iter = 0; 105 | converge =0; 106 | log_out = objective_ONMF_Multi(X_sofar,W,U_total,V,U_star_total{pass},alpha, beta); 107 | % Fix U_star, update U and V 108 | while(iter 0 121 | gradient_u = 2*WW{i}*U{i}*V{i}'*V{i} - 2 * WW{i}*X_block{i}*V{i} + 2*alpha(i)*WW{i}*U{i} - 2*alpha(i)*WW{i}*U_star + beta(i);% + beta(i)*diag(1./D)*U{i}; 122 | Hessian_u = (2*V{i}'*V{i} + 2 * alpha(i)* eye(size(V{i},2))); 123 | U_new{i} = U{i}; 124 | if sum(sum( WW{i} - eye(size(U{i},1)) ~= 0)) > 0 125 | for ii = 1:size(U_new{i},1) 126 | U_new{i}(ii,:) = max(0, U{i}(ii,:) - gamma_u*gradient_u(ii,:)/(WW{i}(ii,ii).*Hessian_u)); % + beta(i)*(inv_norm_u - inv_norm_u^3*uu))); 127 | end 128 | else 129 | U_new{i} = max(0, U{i} - gamma_u*gradient_u/Hessian_u); 130 | end 131 | log_new = norm(W_block{i}*(X_block{i} - U_new{i}*V{i}'), 'fro')^2 ... 132 | +alpha(i)*norm(W_block{i}*(U_new{i} - U_star), 'fro')^2 + beta(i)*sum(sum(abs(U_new{i}))); 133 | diff = log_new - log; 134 | gamma_u = gamma_u/2; 135 | if search_iter > 250 136 | break; 137 | end 138 | search_iter = search_iter + 1; 139 | end 140 | 141 | U{i} = U_new{i}; 142 | % update V 143 | n_sofar = size(X_block{i},1)+block_size*(block_index-1); 144 | U_total{i}(data_range,:) = U{i}; 145 | log = norm(W{i}(1:n_sofar,1:n_sofar)*(X_sofar{i}(1:n_sofar,:) - U_total{i}(1:n_sofar,:)*V{i}'), 'fro')^2; 146 | diff = 1; 147 | gamma_v = 1; 148 | search_iter = 0; 149 | while diff > 0 150 | gradient_v = -2*(A{i} + X_block{i}'*WW{i}*U_new{i}) + 2*V{i}*(B{i} + U_new{i}'*WW{i}*U_new{i}); 151 | Hessian_v = 2*(B{i} + U_new{i}'*WW{i}*U_new{i}); 152 | V_new{i} = max(V{i} - gamma_v*gradient_v/Hessian_v,0); 153 | 154 | log_new = norm(W{i}(1:n_sofar,1:n_sofar)*(X_sofar{i}(1:n_sofar,:) - U_total{i}(1:n_sofar,:)*V_new{i}'), 'fro')^2; 155 | diff = log_new - log; 156 | gamma_v = gamma_v/2; 157 | if search_iter > 250 158 | break; 159 | end 160 | search_iter = search_iter + 1; 161 | end 162 | 163 | V{i} = V_new{i}; 164 | norms = sum(abs(V_new{i}),1); 165 | norms = max(norms,1e-20); 166 | V_new{i} = V_new{i}./repmat(norms,size(V{i},1),1); 167 | U_new{i} = U_new{i}.*repmat(norms,size(U{i},1),1); 168 | U{i} = U_new{i}; 169 | V{i} = V_new{i}; 170 | end 171 | % update U_star 172 | CU = alpha(1)*(W_block{1}.^2)*U_new{1}; 173 | CC = alpha(1)*(W_block{1}.^2); 174 | for i = 2:num_views 175 | CU = CU + alpha(i)*(W_block{i}.^2)*U_new{i}; 176 | CC = CC + alpha(i)*(W_block{i}.^2); 177 | end 178 | CC_inv = diag(1./diag(CC)); 179 | U_star_new = CC_inv*CU; 180 | 181 | U = U_new; 182 | V = V_new; 183 | U_star = U_star_new; 184 | log_out_new = objective_ONMF_Multi(X_sofar,W,U_total,V,U_star_total{pass},alpha, beta); 185 | if abs(log_out_new- log_out)/log_out < tol 186 | converge = 1; 187 | end 188 | log_out = log_out_new; 189 | iter = iter + 1; 190 | end 191 | for i = 1:num_views 192 | A{i} = A{i} + X_block{i}'*W_block{i}.^2*U{i}; 193 | B{i} = B{i} + U{i}'*W_block{i}.^2*U{i}; 194 | end 195 | fprintf('Finish the pass %d, block_index %d, within %d iterations, log is %d\n',pass, block_index,iter, log); 196 | 197 | U_star_total{pass}(data_range,:) = U_star; 198 | for i = 1:num_views 199 | U_total{i}(data_range,:) = U{i}; 200 | end 201 | if skip_loss == 0 202 | Loss(pass, block_index) = log_out/size(X_sofar{1},1); 203 | end 204 | if mod(block_index, 50) == 0 205 | fprintf('Block %d\n', block_index); 206 | end 207 | end 208 | fprintf('Done with pass %d in %d seconds\n', pass, toc(t1)); 209 | replicates = 20; 210 | tmp_nmi = zeros(replicates,1); 211 | tmp_ac = zeros(replicates,1); 212 | for i = 1:replicates 213 | result_tmp = litekmeans(U_star_total{pass}, k, 'Replicates',20); 214 | [tmp_ac(i), tmp_nmi(i), ~] = CalcMetrics(label, result_tmp); 215 | end 216 | average_nmi(pass, 1) = mean(tmp_nmi); 217 | average_ac(pass, 1) = mean(tmp_ac); 218 | fprintf('Done with pass %d\n', pass); 219 | fprintf('The nmi is %d and the AC is %d\n', average_nmi(pass,1), average_ac(pass, 1)); 220 | end -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # online-multiview-clustering-with-incomplete-view 2 | 3 | This project contains the source code for paper Weixiang Shao, Lifang He, Chun-Ta Lu and Philip S. Yu Online Multi-view Clustering with Incomplete Views, IEEE International Conference on Big Data, 2016 The full version of this paper can also be found as arXiv:1611.00481 https://arxiv.org/abs/1611.00481 4 | -------------------------------------------------------------------------------- /bestMap.m: -------------------------------------------------------------------------------- 1 | function [newL2] = bestMap(L1,L2) 2 | %bestmap: permute labels of L2 match L1 as good as possible 3 | % [newL2] = bestMap(L1,L2); 4 | 5 | %=========== 6 | L1 = L1(:); 7 | L2 = L2(:); 8 | if size(L1) ~= size(L2) 9 | error('size(L1) must == size(L2)'); 10 | end 11 | 12 | Label1 = unique(L1); 13 | nClass1 = length(Label1); 14 | Label2 = unique(L2); 15 | nClass2 = length(Label2); 16 | 17 | nClass = max(nClass1,nClass2); 18 | G = zeros(nClass); 19 | for i=1:nClass1 20 | for j=1:nClass2 21 | G(i,j) = length(find(L1 == Label1(i) & L2 == Label2(j))); 22 | end 23 | end 24 | [c,t] = hungarian(-G); 25 | newL2 = zeros(size(L2)); 26 | for i=1:nClass2 27 | newL2(L2 == Label2(i)) = Label1(c(i)); 28 | end 29 | 30 | 31 | return; 32 | 33 | %=======backup old=========== 34 | 35 | L1 = L1 - min(L1) + 1; % min (L1) <- 1; 36 | L2 = L2 - min(L2) + 1; % min (L2) <- 1; 37 | %=========== make bipartition graph ============ 38 | nClass = max(max(L1), max(L2)); 39 | G = zeros(nClass); 40 | for i=1:nClass 41 | for j=1:nClass 42 | G(i,j) = length(find(L1 == i & L2 == j)); 43 | end 44 | end 45 | %=========== assign with hungarian method ====== 46 | [c,t] = hungarian(-G); 47 | newL2 = zeros(nClass,1); 48 | for i=1:nClass 49 | newL2(L2 == i) = c(i); 50 | end -------------------------------------------------------------------------------- /digit/mfeat-mor.txt: -------------------------------------------------------------------------------- 1 | 1.00000000 0.00000000 0.00000000 133.15086140 1.31169276 1620.22177892 2 | 1.00000000 0.00000000 0.00000000 126.72486140 1.30274497 1609.33482208 3 | 1.00000000 0.00000000 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4074.16566155 1916 | 1.00000000 1.00000000 1.00000000 130.85286140 1.43034624 3260.08948999 1917 | 1.00000000 1.00000000 1.00000000 140.61086140 1.54750212 4022.35417426 1918 | 1.00000000 1.00000000 1.00000000 132.47286140 1.41956528 3442.71895672 1919 | 1.00000000 1.00000000 1.00000000 151.16286140 1.54274267 4900.63251141 1920 | 1.00000000 1.00000000 1.00000000 135.57486140 1.43733370 4073.64498836 1921 | 1.00000000 1.00000000 1.00000000 140.40386140 1.63060254 3943.38056624 1922 | 1.00000000 1.00000000 1.00000000 150.36086140 1.50339582 4641.72360440 1923 | 1.00000000 1.00000000 1.00000000 143.02686140 1.61388531 4249.88579260 1924 | 1.00000000 1.00000000 1.00000000 138.90686140 1.38108633 3649.43149165 1925 | 1.00000000 1.00000000 1.00000000 141.97486140 1.46831777 3893.03885890 1926 | 1.00000000 1.00000000 1.00000000 144.99486140 1.58493175 4479.01038962 1927 | 1.00000000 1.00000000 1.00000000 141.25686140 1.56657965 4140.39526435 1928 | 1.00000000 1.00000000 1.00000000 145.86286140 1.36702261 4350.23617458 1929 | 1.00000000 1.00000000 1.00000000 159.35886140 1.61584912 5586.48226340 1930 | 1.00000000 1.00000000 1.00000000 148.45086140 1.62392874 4380.41853474 1931 | 1.00000000 1.00000000 1.00000000 135.16886140 1.53881899 3747.21926173 1932 | 1.00000000 2.00000000 2.00000000 149.89086140 1.65976986 4528.57886190 1933 | 1.00000000 1.00000000 1.00000000 133.38286140 1.53482146 3755.54953190 1934 | 1.00000000 1.00000000 1.00000000 133.56486140 1.34908708 3301.98982923 1935 | 1.00000000 1.00000000 1.00000000 138.65086140 1.42063672 3690.07942579 1936 | 1.00000000 1.00000000 1.00000000 128.23086140 1.30955987 2741.03901000 1937 | 1.00000000 1.00000000 1.00000000 161.50086140 1.57379406 5848.75586630 1938 | 1.00000000 1.00000000 1.00000000 144.51486140 1.56564297 3954.63772771 1939 | 1.00000000 1.00000000 1.00000000 142.09886140 1.50649966 3962.68766034 1940 | 1.00000000 1.00000000 1.00000000 149.63486140 1.54236423 4258.51180153 1941 | 1.00000000 1.00000000 1.00000000 135.95486140 1.41656929 3366.19369442 1942 | 1.00000000 1.00000000 1.00000000 130.92686140 1.23732838 2435.21441600 1943 | 1.00000000 1.00000000 1.00000000 144.71286140 1.46924272 4203.44581191 1944 | 1.00000000 1.00000000 1.00000000 135.72286140 1.48024982 3505.50150001 1945 | 1.00000000 1.00000000 1.00000000 137.22086140 1.50904525 3737.86499584 1946 | 0.00000000 2.00000000 0.00000000 177.75286140 1.94886912 9450.66631764 1947 | 1.00000000 1.00000000 1.00000000 138.55886140 1.48264430 3905.88349701 1948 | 1.00000000 1.00000000 1.00000000 134.68886140 1.33831772 3260.97243004 1949 | 1.00000000 1.00000000 1.00000000 136.87286140 1.41841498 3703.13034691 1950 | 1.00000000 1.00000000 1.00000000 134.92886140 1.32197322 3476.29844333 1951 | 1.00000000 1.00000000 1.00000000 128.59486140 1.28335053 2595.60022779 1952 | 0.00000000 2.00000000 0.00000000 195.86486140 2.06200339 8714.09878135 1953 | 0.00000000 2.00000000 0.00000000 183.90686140 1.97643888 9575.16658020 1954 | 1.00000000 1.00000000 1.00000000 131.68686140 1.47302106 3127.84253896 1955 | 0.00000000 2.00000000 0.00000000 138.73286140 1.39770777 3486.44701557 1956 | 1.00000000 1.00000000 1.00000000 134.59886140 1.52122142 3870.75302081 1957 | 1.00000000 1.00000000 1.00000000 135.18686140 1.35766009 3049.16975900 1958 | 1.00000000 1.00000000 1.00000000 137.18686140 1.45033254 3740.47633317 1959 | 1.00000000 1.00000000 1.00000000 137.50286140 1.40333102 3465.85589552 1960 | 1.00000000 1.00000000 1.00000000 152.52886140 1.46213728 4580.15427612 1961 | 1.00000000 1.00000000 1.00000000 127.37086140 1.46442424 3225.83347319 1962 | 1.00000000 1.00000000 1.00000000 134.48286140 1.30275900 2915.84654880 1963 | 1.00000000 1.00000000 1.00000000 146.49086140 1.44135080 3786.02765766 1964 | 1.00000000 1.00000000 1.00000000 127.08086140 1.32501159 2986.68437693 1965 | 1.00000000 1.00000000 1.00000000 134.78086140 1.42489643 3456.04683324 1966 | 1.00000000 1.00000000 1.00000000 137.04686140 1.44278607 3381.93949131 1967 | 1.00000000 1.00000000 1.00000000 129.30686140 1.37770942 2787.45303795 1968 | 1.00000000 1.00000000 1.00000000 149.29486140 1.50220310 4784.06192183 1969 | 1.00000000 1.00000000 1.00000000 133.87886140 1.34623139 3355.43267496 1970 | 1.00000000 1.00000000 1.00000000 138.77486140 1.48176100 3734.58627414 1971 | 1.00000000 1.00000000 1.00000000 133.20086140 1.46483651 3366.47637640 1972 | 1.00000000 1.00000000 1.00000000 128.35486140 1.33194156 2880.56601621 1973 | 1.00000000 1.00000000 1.00000000 138.78286140 1.45600128 3590.40935171 1974 | 1.00000000 1.00000000 1.00000000 134.81486140 1.39520631 3344.92800460 1975 | 1.00000000 1.00000000 1.00000000 132.15886140 1.51445037 3409.96178763 1976 | 1.00000000 1.00000000 1.00000000 138.71686140 1.39488035 3507.90075771 1977 | 1.00000000 1.00000000 1.00000000 147.70686140 1.55069769 4393.09084635 1978 | 1.00000000 1.00000000 1.00000000 135.99686140 1.44487207 3362.54142546 1979 | 1.00000000 1.00000000 1.00000000 135.04486140 1.33936477 3242.80867370 1980 | 1.00000000 1.00000000 1.00000000 131.88686140 1.36766818 3046.96522533 1981 | 1.00000000 1.00000000 1.00000000 140.51286140 1.51670387 3837.23209637 1982 | 1.00000000 1.00000000 1.00000000 150.59286140 1.49449261 5004.58936897 1983 | 1.00000000 1.00000000 1.00000000 154.22286140 1.54201036 4594.75990722 1984 | 1.00000000 1.00000000 1.00000000 132.96086140 1.41968233 3583.98301349 1985 | 1.00000000 1.00000000 1.00000000 138.17886140 1.42905917 3696.04171885 1986 | 1.00000000 1.00000000 1.00000000 139.21486140 1.61542547 4018.88664567 1987 | 1.00000000 1.00000000 1.00000000 139.61886140 1.38558598 3607.86202134 1988 | 1.00000000 1.00000000 1.00000000 142.99286140 1.52135919 4346.46458848 1989 | 1.00000000 1.00000000 1.00000000 135.64886140 1.46206249 3736.66291173 1990 | 1.00000000 1.00000000 1.00000000 137.02286140 1.34655623 3593.77474835 1991 | 1.00000000 1.00000000 1.00000000 138.70086140 1.47779849 3694.51374331 1992 | 1.00000000 1.00000000 1.00000000 130.04286140 1.36430803 2948.69709163 1993 | 1.00000000 1.00000000 1.00000000 153.25686140 1.66774773 5103.71986701 1994 | 1.00000000 1.00000000 1.00000000 159.26886140 1.58353623 5357.08234250 1995 | 1.00000000 1.00000000 1.00000000 139.88286140 1.47572881 3902.31998508 1996 | 1.00000000 1.00000000 1.00000000 157.49886140 1.65579375 5326.02588861 1997 | 1.00000000 1.00000000 1.00000000 152.40486140 1.62034524 5243.26775360 1998 | 1.00000000 1.00000000 1.00000000 134.67286140 1.54198735 3766.76322195 1999 | 1.00000000 1.00000000 1.00000000 142.92686140 1.42638097 4118.32732012 2000 | 1.00000000 1.00000000 1.00000000 133.92086140 1.56462053 3808.02131663 2001 | -------------------------------------------------------------------------------- /digit/mfeat.info: -------------------------------------------------------------------------------- 1 | The multi-feature digit dataset 2 | ------------------------------- 3 | 4 | Oowned and donated by: 5 | ---------------------- 6 | 7 | Robert P.W. Duin 8 | Department of Applied Physics 9 | Delft University of Technology 10 | P.O. Box 5046, 2600 GA Delft 11 | The Netherlands 12 | 13 | email: duin@ph.tn.tudelft.nl 14 | http : //www.ph.tn.tudelft.nl/~duin 15 | tel +31 15 2786143 16 | 17 | Usage 18 | ----- 19 | A slightly different version of the database is used in 20 | 21 | M. van Breukelen, R.P.W. Duin, D.M.J. Tax, and J.E. den Hartog, Handwritten 22 | digit recognition by combined classifiers, Kybernetika, vol. 34, no. 4, 23 | 1998, 381-386. 24 | 25 | M. van Breukelen and R.P.W. Duin, Neural Network Initialization by Combined 26 | Classifiers, in: A.K. Jain, S. Venkatesh, B.C. Lovell (eds.), ICPR'98, 27 | Proc. 14th Int. Conference on Pattern Recognition (Brisbane, Aug. 16-20), 28 | 29 | The database as it is is used in: 30 | 31 | A.K. Jain, R.P.W. Duin, J. Mao, Statisitcal Pattern Recognition: A Review, 32 | in preparation 33 | 34 | Description 35 | ----------- 36 | 37 | This dataset consists of features of handwritten numerals (`0'--`9') 38 | extracted from a collection of Dutch utility maps. 200 patterns per 39 | class (for a total of 2,000 patterns) have been digitized in binary 40 | images. These digits are represented in terms of the following six 41 | feature sets (files): 42 | 43 | 1. mfeat-fou: 76 Fourier coefficients of the character shapes; 44 | 2. mfeat-fac: 216 end correlations; 45 | 3. mfeat-kar: 64 Karhunen-Love coefficients; 46 | 4. mfeat-pix: 240 pixel averages in 2 x 3 windows; 47 | 5. mfeat-zer: 47 Zernike moments; 48 | 6. mfeat-mor: 6 morphological features. 49 | 50 | In each file the 2000 patterns are stored in ASCI on 2000 lines. The 51 | first 200 patterns are of class `0', followed by sets of 200 patterns 52 | for each of the classes `1' - `9'. Corresponding patterns in different 53 | feature sets (files) correspond to the same original character. 54 | 55 | The source image dataset is lost. Using the pixel-dataset (mfeat-pix) 56 | sampled versions of the original images may be obtained (15 x 16 pixels). 57 | 58 | Total number of instances: 59 | -------------------------- 60 | 2000 (200 instances per class) 61 | 62 | Total number of attributes: 63 | --------------------------- 64 | 649 (distributed over 6 datasets,see above) 65 | 66 | no missing attributes 67 | 68 | Total number of classes: 69 | ------------------------ 70 | 10 71 | 72 | Format: 73 | ------ 74 | 6 files, see above. 75 | Each file contains 2000 lines, one for each instance. 76 | Attributes are SPACE separated and can be loaded by Matlab as 77 | > load filename 78 | No missing attributes. Some are integer, others are real. 79 | 80 | -------------------------------------------------------------------------------- /digit/mfeat.tar: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/software-shao/online-multiview-clustering-with-incomplete-view/c4288607a0ca10488c37469fcb295621476ee66e/digit/mfeat.tar -------------------------------------------------------------------------------- /digit/mfeat/mfeat.info: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/software-shao/online-multiview-clustering-with-incomplete-view/c4288607a0ca10488c37469fcb295621476ee66e/digit/mfeat/mfeat.info -------------------------------------------------------------------------------- /hungarian.m: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/software-shao/online-multiview-clustering-with-incomplete-view/c4288607a0ca10488c37469fcb295621476ee66e/hungarian.m -------------------------------------------------------------------------------- /litekmeans.m: -------------------------------------------------------------------------------- 1 | function [label, center, bCon, sumD, D] = litekmeans(X, k, varargin) 2 | %LITEKMEANS K-means clustering, accelerated by matlab matrix operations. 3 | % 4 | % label = LITEKMEANS(X, K) partitions the points in the N-by-P data matrix 5 | % X into K clusters. This partition minimizes the sum, over all 6 | % clusters, of the within-cluster sums of point-to-cluster-centroid 7 | % distances. Rows of X correspond to points, columns correspond to 8 | % variables. KMEANS returns an N-by-1 vector label containing the 9 | % cluster indices of each point. 10 | % 11 | % [label, center] = LITEKMEANS(X, K) returns the K cluster centroid 12 | % locations in the K-by-P matrix center. 13 | % 14 | % [label, center, bCon] = LITEKMEANS(X, K) returns the bool value bCon to 15 | % indicate whether the iteration is converged. 16 | % 17 | % [label, center, bCon, SUMD] = LITEKMEANS(X, K) returns the 18 | % within-cluster sums of point-to-centroid distances in the 1-by-K vector 19 | % sumD. 20 | % 21 | % [label, center, bCon, SUMD, D] = LITEKMEANS(X, K) returns 22 | % distances from each point to every centroid in the N-by-K matrix D. 23 | % 24 | % [ ... ] = LITEKMEANS(..., 'PARAM1',val1, 'PARAM2',val2, ...) specifies 25 | % optional parameter name/value pairs to control the iterative algorithm 26 | % used by KMEANS. Parameters are: 27 | % 28 | % 'Distance' - Distance measure, in P-dimensional space, that KMEANS 29 | % should minimize with respect to. Choices are: 30 | % {'sqEuclidean'} - Squared Euclidean distance (the default) 31 | % 'cosine' - One minus the cosine of the included angle 32 | % between points (treated as vectors). Each 33 | % row of X SHOULD be normalized to unit. If 34 | % the intial center matrix is provided, it 35 | % SHOULD also be normalized. 36 | % 37 | % 'Start' - Method used to choose initial cluster centroid positions, 38 | % sometimes known as "seeds". Choices are: 39 | % {'sample'} - Select K observations from X at random (the default) 40 | % matrix - A K-by-P matrix of starting locations; or a K-by-1 41 | % indicate vector indicating which K points in X 42 | % should be used as the initial center. In this case, 43 | % you can pass in [] for K, and KMEANS infers K from 44 | % the first dimension of the matrix. 45 | % 46 | % 'MaxIter' - Maximum number of iterations allowed. Default is 100. 47 | % 48 | % 'Replicates' - Number of times to repeat the clustering, each with a 49 | % new set of initial centroids. Default is 1. If the 50 | % initial centroids are provided, the replicate will be 51 | % automatically set to be 1. 52 | % 53 | % 54 | % 55 | % Examples: 56 | % 57 | % fea = rand(500,10); 58 | % [label, center] = litekmeans(fea, 5, 'MaxIter', 50); 59 | % 60 | % fea = rand(500,10); 61 | % [label, center] = litekmeans(fea, 5, 'MaxIter', 50, 'Replicates', 10); 62 | % 63 | % fea = rand(500,10); 64 | % [label, center, bCon, sumD, D] = litekmeans(fea, 5, 'MaxIter', 50); 65 | % TSD = sum(sumD); 66 | % 67 | % fea = rand(500,10); 68 | % initcenter = rand(5,10); 69 | % [label, center] = litekmeans(fea, 5, 'MaxIter', 50, 'Start', initcenter); 70 | % 71 | % fea = rand(500,10); 72 | % idx=randperm(500); 73 | % [label, center] = litekmeans(fea, 5, 'MaxIter', 50, 'Start', idx(1:5)); 74 | % 75 | % 76 | % See also KMEANS 77 | % 78 | % version 2.0 --December/2011 79 | % version 1.0 --November/2011 80 | % 81 | % Written by Deng Cai (dengcai AT gmail.com) 82 | 83 | 84 | if nargin < 2 85 | error('litekmeans:TooFewInputs','At least two input arguments required.'); 86 | end 87 | 88 | [n, p] = size(X); 89 | 90 | 91 | pnames = { 'distance' 'start' 'maxiter' 'replicates' 'onlinephase'}; 92 | dflts = {'sqeuclidean' 'sample' [] [] 'off' }; 93 | [eid,errmsg,distance,start,maxit,reps,online] = getargs(pnames, dflts, varargin{:}); 94 | if ~isempty(eid) 95 | error(sprintf('litekmeans:%s',eid),errmsg); 96 | end 97 | 98 | if ischar(distance) 99 | distNames = {'sqeuclidean','cosine'}; 100 | j = strcmpi(distance, distNames); 101 | j = find(j); 102 | if length(j) > 1 103 | error('litekmeans:AmbiguousDistance', ... 104 | 'Ambiguous ''Distance'' parameter value: %s.', distance); 105 | elseif isempty(j) 106 | error('litekmeans:UnknownDistance', ... 107 | 'Unknown ''Distance'' parameter value: %s.', distance); 108 | end 109 | distance = distNames{j}; 110 | else 111 | error('litekmeans:InvalidDistance', ... 112 | 'The ''Distance'' parameter value must be a string.'); 113 | end 114 | 115 | 116 | center = []; 117 | if ischar(start) 118 | startNames = {'sample','cluster'}; 119 | j = find(strncmpi(start,startNames,length(start))); 120 | if length(j) > 1 121 | error(message('litekmeans:AmbiguousStart', start)); 122 | elseif isempty(j) 123 | error(message('litekmeans:UnknownStart', start)); 124 | elseif isempty(k) 125 | error('litekmeans:MissingK', ... 126 | 'You must specify the number of clusters, K.'); 127 | end 128 | if j == 2 129 | if floor(.1*n) < 5*k 130 | j = 1; 131 | end 132 | end 133 | start = startNames{j}; 134 | elseif isnumeric(start) 135 | if size(start,2) == p 136 | center = start; 137 | elseif (size(start,2) == 1 || size(start,1) == 1) 138 | center = X(start,:); 139 | else 140 | error('litekmeans:MisshapedStart', ... 141 | 'The ''Start'' matrix must have the same number of columns as X.'); 142 | end 143 | if isempty(k) 144 | k = size(center,1); 145 | elseif (k ~= size(center,1)) 146 | error('litekmeans:MisshapedStart', ... 147 | 'The ''Start'' matrix must have K rows.'); 148 | end 149 | start = 'numeric'; 150 | else 151 | error('litekmeans:InvalidStart', ... 152 | 'The ''Start'' parameter value must be a string or a numeric matrix or array.'); 153 | end 154 | 155 | % The maximum iteration number is default 100 156 | if isempty(maxit) 157 | maxit = 100; 158 | end 159 | 160 | % Assume one replicate 161 | if isempty(reps) || ~isempty(center) 162 | reps = 1; 163 | end 164 | 165 | if ~(isscalar(k) && isnumeric(k) && isreal(k) && k > 0 && (round(k)==k)) 166 | error('litekmeans:InvalidK', ... 167 | 'X must be a positive integer value.'); 168 | elseif n < k 169 | error('litekmeans:TooManyClusters', ... 170 | 'X must have more rows than the number of clusters.'); 171 | end 172 | 173 | 174 | bestlabel = []; 175 | sumD = zeros(1,k); 176 | bCon = false; 177 | 178 | for t=1:reps 179 | switch start 180 | case 'sample' 181 | center = X(randsample(n,k),:); 182 | case 'cluster' 183 | Xsubset = X(randsample(n,floor(.1*n)),:); 184 | [dump, center] = litekmeans(Xsubset, k, varargin{:}, 'start','sample', 'replicates',1); 185 | case 'numeric' 186 | end 187 | 188 | last = 0;label=1; 189 | it=0; 190 | 191 | switch distance 192 | case 'sqeuclidean' 193 | while any(label ~= last) && it1 224 | if it>=maxit 225 | aa = full(sum(X.*X,2)); 226 | bb = full(sum(center.*center,2)); 227 | ab = full(X*center'); 228 | D = bsxfun(@plus,aa,bb') - 2*ab; 229 | D(D<0) = 0; 230 | else 231 | aa = full(sum(X.*X,2)); 232 | D = aa(:,ones(1,k)) + D; 233 | D(D<0) = 0; 234 | end 235 | D = sqrt(D); 236 | for j = 1:k 237 | sumD(j) = sum(D(label==j,j)); 238 | end 239 | bestsumD = sumD; 240 | bestD = D; 241 | end 242 | else 243 | if it>=maxit 244 | aa = full(sum(X.*X,2)); 245 | bb = full(sum(center.*center,2)); 246 | ab = full(X*center'); 247 | D = bsxfun(@plus,aa,bb') - 2*ab; 248 | D(D<0) = 0; 249 | else 250 | aa = full(sum(X.*X,2)); 251 | D = aa(:,ones(1,k)) + D; 252 | D(D<0) = 0; 253 | end 254 | D = sqrt(D); 255 | for j = 1:k 256 | sumD(j) = sum(D(label==j,j)); 257 | end 258 | if sum(sumD) < sum(bestsumD) 259 | bestlabel = label; 260 | bestcenter = center; 261 | bestsumD = sumD; 262 | bestD = D; 263 | end 264 | end 265 | case 'cosine' 266 | while any(label ~= last) && it1 291 | if any(label ~= last) 292 | W=full(X*center'); 293 | end 294 | D = 1-W; 295 | for j = 1:k 296 | sumD(j) = sum(D(label==j,j)); 297 | end 298 | bestsumD = sumD; 299 | bestD = D; 300 | end 301 | else 302 | if any(label ~= last) 303 | W=full(X*center'); 304 | end 305 | D = 1-W; 306 | for j = 1:k 307 | sumD(j) = sum(D(label==j,j)); 308 | end 309 | if sum(sumD) < sum(bestsumD) 310 | bestlabel = label; 311 | bestcenter = center; 312 | bestsumD = sumD; 313 | bestD = D; 314 | end 315 | end 316 | end 317 | end 318 | 319 | label = bestlabel; 320 | center = bestcenter; 321 | if reps>1 322 | sumD = bestsumD; 323 | D = bestD; 324 | elseif nargout > 3 325 | switch distance 326 | case 'sqeuclidean' 327 | if it>=maxit 328 | aa = full(sum(X.*X,2)); 329 | bb = full(sum(center.*center,2)); 330 | ab = full(X*center'); 331 | D = bsxfun(@plus,aa,bb') - 2*ab; 332 | D(D<0) = 0; 333 | else 334 | aa = full(sum(X.*X,2)); 335 | D = aa(:,ones(1,k)) + D; 336 | D(D<0) = 0; 337 | end 338 | D = sqrt(D); 339 | case 'cosine' 340 | if it>=maxit 341 | W=full(X*center'); 342 | end 343 | D = 1-W; 344 | end 345 | for j = 1:k 346 | sumD(j) = sum(D(label==j,j)); 347 | end 348 | end 349 | 350 | 351 | 352 | 353 | function [eid,emsg,varargout]=getargs(pnames,dflts,varargin) 354 | %GETARGS Process parameter name/value pairs 355 | % [EID,EMSG,A,B,...]=GETARGS(PNAMES,DFLTS,'NAME1',VAL1,'NAME2',VAL2,...) 356 | % accepts a cell array PNAMES of valid parameter names, a cell array 357 | % DFLTS of default values for the parameters named in PNAMES, and 358 | % additional parameter name/value pairs. Returns parameter values A,B,... 359 | % in the same order as the names in PNAMES. Outputs corresponding to 360 | % entries in PNAMES that are not specified in the name/value pairs are 361 | % set to the corresponding value from DFLTS. If nargout is equal to 362 | % length(PNAMES)+1, then unrecognized name/value pairs are an error. If 363 | % nargout is equal to length(PNAMES)+2, then all unrecognized name/value 364 | % pairs are returned in a single cell array following any other outputs. 365 | % 366 | % EID and EMSG are empty if the arguments are valid. If an error occurs, 367 | % EMSG is the text of an error message and EID is the final component 368 | % of an error message id. GETARGS does not actually throw any errors, 369 | % but rather returns EID and EMSG so that the caller may throw the error. 370 | % Outputs will be partially processed after an error occurs. 371 | % 372 | % This utility can be used for processing name/value pair arguments. 373 | % 374 | % Example: 375 | % pnames = {'color' 'linestyle', 'linewidth'} 376 | % dflts = { 'r' '_' '1'} 377 | % varargin = {{'linew' 2 'nonesuch' [1 2 3] 'linestyle' ':'} 378 | % [eid,emsg,c,ls,lw] = statgetargs(pnames,dflts,varargin{:}) % error 379 | % [eid,emsg,c,ls,lw,ur] = statgetargs(pnames,dflts,varargin{:}) % ok 380 | 381 | % We always create (nparams+2) outputs: 382 | % one each for emsg and eid 383 | % nparams varargs for values corresponding to names in pnames 384 | % If they ask for one more (nargout == nparams+3), it's for unrecognized 385 | % names/values 386 | 387 | % Original Copyright 1993-2008 The MathWorks, Inc. 388 | % Modified by Deng Cai (dengcai@gmail.com) 2011.11.27 389 | 390 | 391 | 392 | 393 | % Initialize some variables 394 | emsg = ''; 395 | eid = ''; 396 | nparams = length(pnames); 397 | varargout = dflts; 398 | unrecog = {}; 399 | nargs = length(varargin); 400 | 401 | % Must have name/value pairs 402 | if mod(nargs,2)~=0 403 | eid = 'WrongNumberArgs'; 404 | emsg = 'Wrong number of arguments.'; 405 | else 406 | % Process name/value pairs 407 | for j=1:2:nargs 408 | pname = varargin{j}; 409 | if ~ischar(pname) 410 | eid = 'BadParamName'; 411 | emsg = 'Parameter name must be text.'; 412 | break; 413 | end 414 | i = strcmpi(pname,pnames); 415 | i = find(i); 416 | if isempty(i) 417 | % if they've asked to get back unrecognized names/values, add this 418 | % one to the list 419 | if nargout > nparams+2 420 | unrecog((end+1):(end+2)) = {varargin{j} varargin{j+1}}; 421 | % otherwise, it's an error 422 | else 423 | eid = 'BadParamName'; 424 | emsg = sprintf('Invalid parameter name: %s.',pname); 425 | break; 426 | end 427 | elseif length(i)>1 428 | eid = 'BadParamName'; 429 | emsg = sprintf('Ambiguous parameter name: %s.',pname); 430 | break; 431 | else 432 | varargout{i} = varargin{j+1}; 433 | end 434 | end 435 | end 436 | 437 | varargout{nparams+1} = unrecog; 438 | -------------------------------------------------------------------------------- /load_data.m: -------------------------------------------------------------------------------- 1 | function [Data, info] = load_data(data_name, random, incomplete_rate) 2 | if strcmp(data_name,'digit') 3 | D = cell(5,1); 4 | D{1} = load(strcat('digit/mfeat-fou.txt')); 5 | D{2} = load(strcat('digit/mfeat-fac.txt')); 6 | D{3} = load(strcat('digit/mfeat-pix.txt')); 7 | D{4} = load(strcat('digit/mfeat-mor.txt')); 8 | D{5} = load(strcat('digit/mfeat-zer.txt')); 9 | num_views = 5; 10 | label = zeros(size(D{1},1),1); 11 | for i=1:10 12 | label(1+(i-1)*200:200*i) = i; 13 | end 14 | if random==1 15 | p = randperm(numel(label)); 16 | label = label(p); 17 | for i = 1:num_views 18 | D{i} = D{i}(p,:); 19 | end 20 | Data = D; 21 | info.size = numel(label); 22 | info.label= label; 23 | info.num_views = num_views; 24 | else 25 | 26 | end 27 | % based on the incomplete rate, make the data incomplete. 28 | if incomplete_rate > 0 29 | p = randperm(numel(label)-1); 30 | p = p+1; 31 | p = repmat(p, 1,num_views); 32 | index = cell(num_views,1); 33 | incomplete_size = ceil(incomplete_rate*numel(label)); 34 | for i = 1:num_views 35 | index{i} = p((i-1)*incomplete_size+1:i*incomplete_size); 36 | index{i} = sort(index{i}); 37 | end 38 | for i = 1:num_views 39 | for j = 1:numel(index{i}) 40 | total = sum(Data{i}(1:index{i}(j)-1,:),1); 41 | incomplete = sum(Data{i}(index{i}(1:j-1),:),1); 42 | Data{i}(index{i}(j),:) = (total - incomplete)./(index{i}(j)-j); 43 | end 44 | end 45 | info.incomplete_index = index; 46 | end 47 | info.num_views = 5; 48 | end 49 | end -------------------------------------------------------------------------------- /nmi.m: -------------------------------------------------------------------------------- 1 | function v = nmi(label, result) 2 | % Nomalized mutual information 3 | % Written by Mo Chen (mochen@ie.cuhk.edu.hk). March 2009. 4 | %assert(length(label) == length(result)); 5 | 6 | label = label(:); 7 | result = result(:); 8 | 9 | n = length(label); 10 | 11 | label_unique = unique(label); 12 | result_unique = unique(result); 13 | 14 | % check the integrity of result 15 | %if length(label_unique) ~= length(result_unique) 16 | % error('The clustering result is not consistent with label.'); 17 | %end; 18 | 19 | c = length(label_unique); 20 | 21 | % distribution of result and label 22 | Ml = double(repmat(label,1,c) == repmat(label_unique',n,1)); 23 | %Mr = double(repmat(result,1,c) == repmat(result_unique',n,1)); 24 | Mr = double(repmat(result,1,c) == repmat(label_unique',n,1)); 25 | Pl = sum(Ml)/n; 26 | Pr = sum(Mr)/n; 27 | 28 | % entropy of Pr and Pl 29 | Hl = -sum( Pl .* log2( Pl + eps ) ); 30 | Hr = -sum( Pr .* log2( Pr + eps ) ); 31 | 32 | 33 | % joint entropy of Pr and Pl 34 | % M = zeros(c); 35 | % for I = 1:c 36 | % for J = 1:c 37 | % M(I,J) = sum(result==result_unique(I)&label==label_unique(J)); 38 | % end; 39 | % end; 40 | % M = M / n; 41 | M = Ml'*Mr/n; 42 | Hlr = -sum( M(:) .* log2( M(:) + eps ) ); 43 | 44 | % mutual information 45 | MI = Hl + Hr - Hlr; 46 | 47 | % normalized mutual information 48 | v = sqrt((MI/Hl)*(MI/Hr)) ; -------------------------------------------------------------------------------- /objective_ONMF_Multi.m: -------------------------------------------------------------------------------- 1 | function [o] = objective_ONMF_Multi(X,W,U,V,U_star,alpha,beta) 2 | num_views = numel(X); 3 | o = 0; 4 | U_star = U_star(1:size(X{1},1),:); 5 | for i = 1:num_views 6 | W{i} = W{i}(1:size(X{i},1), 1:size(X{i},1)); 7 | U{i} = U{i}(1:size(X{i},1),:); 8 | o = o + norm(W{i}*(X{i} - U{i}*V{i}'), 'fro')^2 + alpha(i)*norm(W{i}*(U{i} - U_star), 'fro')^2 + beta(i)*sum(sum(abs(U{i}))); 9 | end -------------------------------------------------------------------------------- /run.m: -------------------------------------------------------------------------------- 1 | clear 2 | warning off; 3 | rng(1); 4 | 5 | addpath('mulNMF/'); 6 | addpath('mulNMF_incomplete_2/'); 7 | 8 | data_set_name = 'digit'; 9 | block_size = 50; 10 | random = 1; 11 | num_clusters = 10; 12 | maxiter = 200; 13 | decay = 1; 14 | tol = 1e-4; 15 | num_passes = 2; 16 | skip_loss = 0; 17 | incomplete_range = 0.4:0.2:0.4; 18 | average_nmi = cell(numel(incomplete_range),1); 19 | average_ac = cell(numel(incomplete_range),1); 20 | nmf_ac = zeros(numel(incomplete_range),20); 21 | nmf_nmi = nmf_ac; 22 | 23 | [Data, info] = load_data(data_set_name, random, 0.4); 24 | num_views = info.num_views; 25 | alpha = 1e-2 * ones(num_views,1); 26 | beta = 1e-7 * ones(num_views,1); 27 | for i = 1:num_views 28 | Data{i}(Data{i}<0) = 0; 29 | Data{i} = Data{i}./sum(sum(Data{i})); 30 | end 31 | 32 | % Construct W 33 | W = cell(numel(Data),1); 34 | if exist('info.W') 35 | for i = 1:numel(Data) 36 | W{i} = info.W{i}; 37 | end 38 | info.W = []; 39 | else 40 | for i = 1:numel(Data) 41 | W{i} = diag(sparse(ones(info.size,1))); 42 | if exist('info.incomplete_index') 43 | counter = 0; 44 | for j = 1:numel(info.incomplete_index{i}) 45 | counter = counter +1; 46 | W{i}(info.incomplete_index{i}(j),info.incomplete_index{i}(j)) = 1.0*counter/info.incomplete_index{i}(j); 47 | end 48 | end 49 | end 50 | end 51 | option.label = info.label; 52 | option.k = num_clusters; 53 | option.maxiter = maxiter; 54 | option.tol = tol; 55 | option.num_cluster = num_clusters; 56 | option.decay = decay; 57 | option.alpha = alpha; 58 | option.beta = beta; 59 | option.pass = num_passes; 60 | option.loss = skip_loss; 61 | t1 = tic; 62 | [U_total,V, U_star, Loss, average_nmi, average_ac] = ONMF_Multi_PGD_search(Data, W, option, block_size); 63 | display(average_nmi(:,1)'); 64 | display(average_ac(:,1)'); 65 | toc(t1) 66 | 67 | 68 | 69 | --------------------------------------------------------------------------------