├── 100Leaves.mat ├── 3Sources.mat ├── ACM.mat ├── ALOI.mat ├── BBC4view.mat ├── BBCSport.mat ├── Caltech101-7.mat ├── CiteSeer.mat ├── Cora.mat ├── Handwritten.mat ├── MNIST-10k.mat ├── MNIST-4.mat ├── MSRC-v5.mat ├── Movies.mat ├── NUS-WIDE.mat ├── OutdoorScene.mat ├── Prokaryotic.mat ├── ProteinFold.mat ├── README.md ├── Reuters-1200.mat ├── Reuters-1500.mat ├── UCI.mat ├── WebKB.mat ├── Wikipedia-test.mat ├── Wikipedia.mat └── Yale.mat /100Leaves.mat: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ChuanbinZhang/Multi-view-datasets/03ead31e6538322e4e0701c3c28a01595c1a015d/100Leaves.mat -------------------------------------------------------------------------------- /3Sources.mat: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ChuanbinZhang/Multi-view-datasets/03ead31e6538322e4e0701c3c28a01595c1a015d/3Sources.mat -------------------------------------------------------------------------------- /ACM.mat: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ChuanbinZhang/Multi-view-datasets/03ead31e6538322e4e0701c3c28a01595c1a015d/ACM.mat -------------------------------------------------------------------------------- /ALOI.mat: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ChuanbinZhang/Multi-view-datasets/03ead31e6538322e4e0701c3c28a01595c1a015d/ALOI.mat -------------------------------------------------------------------------------- /BBC4view.mat: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ChuanbinZhang/Multi-view-datasets/03ead31e6538322e4e0701c3c28a01595c1a015d/BBC4view.mat -------------------------------------------------------------------------------- /BBCSport.mat: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ChuanbinZhang/Multi-view-datasets/03ead31e6538322e4e0701c3c28a01595c1a015d/BBCSport.mat -------------------------------------------------------------------------------- /Caltech101-7.mat: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ChuanbinZhang/Multi-view-datasets/03ead31e6538322e4e0701c3c28a01595c1a015d/Caltech101-7.mat -------------------------------------------------------------------------------- /CiteSeer.mat: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ChuanbinZhang/Multi-view-datasets/03ead31e6538322e4e0701c3c28a01595c1a015d/CiteSeer.mat -------------------------------------------------------------------------------- /Cora.mat: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ChuanbinZhang/Multi-view-datasets/03ead31e6538322e4e0701c3c28a01595c1a015d/Cora.mat -------------------------------------------------------------------------------- /Handwritten.mat: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ChuanbinZhang/Multi-view-datasets/03ead31e6538322e4e0701c3c28a01595c1a015d/Handwritten.mat -------------------------------------------------------------------------------- /MNIST-10k.mat: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ChuanbinZhang/Multi-view-datasets/03ead31e6538322e4e0701c3c28a01595c1a015d/MNIST-10k.mat -------------------------------------------------------------------------------- /MNIST-4.mat: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ChuanbinZhang/Multi-view-datasets/03ead31e6538322e4e0701c3c28a01595c1a015d/MNIST-4.mat -------------------------------------------------------------------------------- /MSRC-v5.mat: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ChuanbinZhang/Multi-view-datasets/03ead31e6538322e4e0701c3c28a01595c1a015d/MSRC-v5.mat -------------------------------------------------------------------------------- /Movies.mat: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ChuanbinZhang/Multi-view-datasets/03ead31e6538322e4e0701c3c28a01595c1a015d/Movies.mat -------------------------------------------------------------------------------- /NUS-WIDE.mat: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ChuanbinZhang/Multi-view-datasets/03ead31e6538322e4e0701c3c28a01595c1a015d/NUS-WIDE.mat -------------------------------------------------------------------------------- /OutdoorScene.mat: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ChuanbinZhang/Multi-view-datasets/03ead31e6538322e4e0701c3c28a01595c1a015d/OutdoorScene.mat -------------------------------------------------------------------------------- /Prokaryotic.mat: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ChuanbinZhang/Multi-view-datasets/03ead31e6538322e4e0701c3c28a01595c1a015d/Prokaryotic.mat -------------------------------------------------------------------------------- /ProteinFold.mat: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ChuanbinZhang/Multi-view-datasets/03ead31e6538322e4e0701c3c28a01595c1a015d/ProteinFold.mat -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | 2 | # Multi-view Datasets 3 | ## 1. Usage 4 | All dataset files contain two attributes, `X` and `y`. 5 | - `X` is the multi-view data as a cell, each element in this cell is an _N_-by-_D_ matrix _Xk_, where _N_ is the number of data points and _D_ is the feature dimensions. So rows of _Xk_ correspond to data points. 6 | - `y` is the vector of ground-truth labels. 7 | 8 | **Note: Due to Github's limitation on file size, files larger than 20MB will be stored in other repositories**： 9 | 1. [Baidu](https://pan.baidu.com/s/1vAIi4Tdhuk9c3XNf-48_4Q), code：yeog 10 | 2. [Google](https://drive.google.com/drive/folders/1wkQA0z-cAKVzFIk1izOALCXdqdwsPvx2?usp=share_link) 11 | 12 | ## 2. Dataset Details 13 | | Dataset | Instances | Clusters | Views(dimension) | Description | Type | 14 | |---------|-----------|----------|------------------|-------------|----------------| 15 | | 3Sources | 169 | 6 | Reuters(3068), BBC(3560), Guardian(3631) | A new multi-view text dataset collected from three well-known online news sources: [BBC](http://mlg.ucd.ie/datasets/news.bbc.co.uk), [Reuters](http://reuters.co.uk/),and [The Guardian](http://www.guardian.co.uk/) [[14]](#weiFuzzyClusteringMultiview2022). | Text | 16 | | 100Leaves | 1600 | 100 | SD(64), FSM(64), TH(64) | Sixteen samples of leaf each of one-hundred plant species [[16]](#yangMultiViewAdjacencyConstrainedHierarchical2022). | Image | 17 | | ACM | 3025 | 3 | View1(1870), View2(3025), View2(3025), View2(3025), View2(3025) | The dataset extracts papers published in KDD, SIGMOD, SIGCOMM, MobiCOMM, and VLDB. | | 18 | | ALOI-1k | 110250 | 1000 | CS(77), Haralick(13), HSV(64), RGB(125) | The [Amsterdam Library of Object Images](http://aloi.science.uva.nl/) is a collection of 110250 images of 1000 small objects, taken under various light conditions and rotation angles [[25]](#zhangFacilitatedLowrankMultiview2023). | Object | 19 | | ALOI | 10800 | 100 | CS(77), Haralick(13), HSV(64), RGB(125) | A subset of ALOI-1k [[25]](#zhangFacilitatedLowrankMultiview2023). | Object | 20 | | Animal | 11673 | 20 | View1(2688), View2(2000), View3(2001), View4(2000), | A selected set of the Animals with Attributes dataset [[12]](#lampertLearningDetectUnseen2009). | Animal | 21 | | BBCSport | 544 | 5 | View1(3183), View2(3203) | This document dataset contains 544 documents from the BBC Sport website, and they are aboutthe sports news between 2004 and 2005 [[4]](#chenLowrankTensorBased2022). | Text | 22 | | BBC4view | 685 | 5 | View1(4659), View2(4633), View3(4665), View4(4684) | Similar to BBCSport, this dataset consists of 685 documents from the BBC Sport website about sports news [[4]](#chenLowrankTensorBased2022). | Text | 23 | | COIL20 | 1440 | 20 | Intensity(1024), LBP(944), Gabor(4096) | It is the abbreviation of the Columbia object image library dataset [[24]](#wuEssentialTensorLearning2019). | Object | 24 | | Caltech101-7 | 1474 | 7 | GABOR(48), WM(40), CENT(254), HOG(1984), GIST(512), LBP(928) | This dataset contains 1474 images belonging to seven classes, which are faces, motorbikes, dollar bill, Garfield, stop sign, and windsor chair [[4]](#chenLowrankTensorBased2022). | Object | 25 | | Caltech101-20 | 2386 | 20 | GABOR(48), WM(40), CENT(254), HOG(1984), GIST(512), LBP(928) | This is the frequently used subsets of Caltech101 consisting of 20 categories of images built for object recognition tasks [[5]](#liMultiviewClusteringScalable2022). | Object | 26 | | Caltech101-all | 9144 | 102 | GABOR(48), WM(40), CENT(254), HOG(1984), GIST(512), LBP(928) | The Caltech101 dataset contains images from 101 object categories and **background** (e.g., “helicopter”, “elephant” and “chair” etc.) and a background category that contains the images not from the 101 object categories [[8]](#zhangBinaryMultiViewClustering2019). | Object | 27 | | CiteSeer | 3312 | 6 | Content(3703), Cites(4732) | The archive contains 3312 documents over the 6 labels (Agents,IR,DB,AI,HCI,ML) [[13]](#fangEfficientMultiviewClustering2023). | Text | 28 | | Cora | 2708 | 7 | Content(1433), Inbound(2708), Outbound(2708), Cites(2708) | The archive contains 2708 documents over the 7 labels (Neural_Networks, Rule_Learning, Reinforcement_Learning, Probabilistic_Methods, Theory, Genetic_Algorithms,Case_Based) [[13]](#fangEfficientMultiviewClustering2023). | Text | 29 | | Handwritten | 2000 | 10 | FOU(76), FAC(216), KAR(64), PIX(240), ZER(47), MOR(6) | This dataset consists of features of handwritten numerals ('0'--'9') extracted from a collection of Dutch utility maps [[7]](#nieMultiviewClusteringAdaptively2018). | Image | 30 | | MNIST-10k | 10000 | 10 | ISO(30), LDA(9), NPE(30) | A freely available and well-known handwritten database for image recognition consisting of four categories from digit 0 to digit 9, and each category has 1,000 samples evenly [[1]](#dengMNISTDatabaseHandwritten2012). | Handwritten | 31 | | MNIST-4 | 4000 | 4 | ISO(30), LDA(9), NPE(30) | MNIST-4 is a subset of MNIST-10k consisting of four categories from digit 0 to digit 3 [[5]](#liMultiviewClusteringScalable2022). | Handwritten | 32 | | Movies | 617 | 17 | Keywords(1878), Actors(1398) | It is a movie corpus extracted from IMDb. [[22]](#xieGeneralizedMultiviewLearning2022) | Text | 33 | | MSRC-v5 | 210 | 7 | CM(24), HOG(576), GIST(512), LBP(256), CENT(254) | A subset of the Microsoft Research in Cambridge dataset [[21]](#xiaTensorizedBipartiteGraph2023). | Image | 34 | | NUS-WIDE-OBJ | 30000 | 31 | CH(65), CM(226), CORR(145), EDH(74), WT(129) | It is a dataset for object recognition which consists of 30000 images in 31 classes[[9]](#liLargescaleMultiviewSpectral2015). | Image | 35 | | NUS-WIDE | 2400 | 12 | CH(64), CORR(144), EDH(75), WT(128), CM55(225) | 12 categories of animal images selected from the NUS-WIDE-OBJ dataset, and the first 200 images are selected for each category [[3]](#chuaNUSWIDERealworldWeb2009). | Object | 36 | | ORL | 400 | 40 | View1(4096), View2(3304), View3(6750) | Face dataset contains 400 images of 40 distinct subjects. For each category, images were taken at different times, lights, facial expressions (open / closed eyes, smiling or not) and facial details (with glasses / without glasses) [[23]](#luoConsistentSpecificMultiView2018) .| Face | 37 | | OutdoorScene | 2688 | 8 | GIST(512), HOG(432), LBP(256), Gabor(48) | The dataset has 2688 outdoor scene images consisting of 8 groups [[13]](#fangEfficientMultiviewClustering2023). | Object | 38 | | ProteinFold | 694 | 27 | View1(27), View2(27), View3(27), View4(27), View5(27), View6(27), View7(27), View8(27), View9(27), View10(27), View11(27), View12(27) | Multiply kernel learning dataset on protein fold prediction [[19]](#liuEfficientEffectiveRegularized2021). | Protein | 39 | | Prokaryotic | 551 | 4 | gene-repert(393), proteome-comp(3), Text(438) | It contains prokaryotic species described with heterogeneous multi-view data including textual data and different genomic representations.[[20]](#niuMultiviewEnsembleClustering2023) | Genome | 40 | | Reuters-1200 | 1200 | 6 | English(2000), French(2000), German(2000), Spanish(2000), Italian(2000) | It contains 6 samples of 1200 documents over 6 labels, and desribed by 5 views of 2000 words each [[15]](#huangAutoweightedMultiviewClustering2020). | Text | 41 | | Reuters-1500 | 1500 | 6 | English(21531), French(24893), German(34279), Spanish(15506), Italian(11547) | The dataset is collection of 1500 documents which are expressed in five different languages (Italian, Spanish, French, German and English) and the corresponding translations [[14]](#weiFuzzyClusteringMultiview2022). | Text | 42 | | Reuters | 18758 | 6 | English(21531), French(24893), German (34279), Spanish (15506), Italian(11547) | This dataset consists of documents that are written in five different languages and their translations[[9]](#liLargescaleMultiviewSpectral2015). | Text | 43 | | UCI | 2000 | 10 | Intensity(240), FOU(76), MOR(6) | Obtained from the UCI machine learning repository, this dataset consists of 2000 handwritten digit images belonging to 10 digits with each digit containing 200 samples [[4]](#chenLowrankTensorBased2022). | Handwritten | 44 | | WebKB | 1051 | 2 | Anchor(1,840), Content(3,000) | The dataset for web pages collected from computer science department web sites at four universities: Cornell University, University of Washington,University of Wisconsin, and University of Texas [[6]](#qiangFastMultiviewDiscrete2021). | Text | 45 | | Wikipedia | 2866 | 10 | Word(128), SIFT(10) | Wikipedia dataset is the most widely-used dataset for cross-media retrieval. It is based on Wikipedia’s "featured articles", a continually updated article collection [[11]](#rasiwasiaNewApproachCrossmodal2010). | Text | 46 | | Wikipedia-test | 693 | 10 | Word(128), SIFT(10) | The test set of the Wikipedia collection [[10]](#shiFastMultiViewClustering2023). | Text | 47 | | Yale | 165 | 15 | Intensity(4096), LBP(3304), Gabor(6750) | This dataset consists of 165 gray-scale face images belonging to 15 subjects with each subject containing 11 images [[4]](#chenLowrankTensorBased2022). | Face | 48 | 49 | ### List of features 50 | - FOU: Fourier coefficients of the character shapes 51 | - FAC: Profile correlations 52 | - PIX: Pixel averages in 2 × 3 windows 53 | - ZER: Zernike moment 54 | - MOR: Morphological features. 55 | - Gabor: Gabor feature 56 | - WM: Wavelet moments 57 | - CENT: CENTRIST feature [17] 58 | - HOG: Histogram of oriented gradients feature 59 | - GIST: GIST feature [18] 60 | - LBP: Local binary patterns feature 61 | - CH: Color Histogram 62 | - TH: Texture Histogram 63 | - CM: Color moments 64 | - CS: Color similiarity 65 | - CORR: Color correlation 66 | - EDH: Edge distribution 67 | - WT: Wavelet texture 68 | - SD: Shape descriptor 69 | - FSM: Fine scale margin 70 | - SIFT: Scale Invariant Feature Transform 71 | 72 | ## References 73 |
74 | 75 | [1] L. Deng, “The MNIST Database of Handwritten Digit Images for Machine Learning Research [Best of the Web],” _IEEE Signal Processing Magazine_, vol. 29, no. 6, pp. 141–142, Jan. 2012, doi: [10.1109/MSP.2012.2211477](https://doi.org/10.1109/MSP.2012.2211477). 76 | 77 |
78 | 79 | [2] J. Winn and N. Jojic, “LOCUS: learning object classes with unsupervised segmentation,” in _Tenth IEEE International Conference on Computer Vision (ICCV’05)_ Volume 1, Oct. 2005, pp. 756-763 Vol. 1. doi: [10.1109/ICCV.2005.148](https://doi.org/10.1109/ICCV.2005.148). 80 | 81 |
82 | 83 | [3] B. Wang, Y. Xiao, Z. Li, X. Wang, X. Chen, and D. Fang, “Robust Self-Weighted Multi-View Projection Clustering,” in _Proc. AAAI Conf. Artif. Intell._, Apr. 2020, pp. 6110–6117. doi: [10.1609/aaai.v34i04.6075](https://doi.org/10.1609/aaai.v34i04.6075). 84 | 85 |
86 | 87 | [4] M.-S. Chen, C.-D. Wang, and J.-H. Lai, “Low-rank Tensor Based Proximity Learning for Multi-view Clustering,” _IEEE Transactions on Knowledge and Data Engineering_, pp. 1–1, Jan. 2022, doi: [10.1109/TKDE.2022.3151861](https://doi.org/10.1109/TKDE.2022.3151861). 88 | 89 |
90 | 91 | [5] X. Li, H. Zhang, R. Wang, and F. Nie, “Multiview Clustering: A Scalable and Parameter-Free Bipartite Graph Fusion Method,” _IEEE Transactions on Pattern Analysis and Machine Intelligence_, vol. 44, no. 1, pp. 330–344, Jan. 2022, doi: [10.1109/TPAMI.2020.3011148](https://doi.org/10.1109/TPAMI.2020.3011148). 92 | 93 |
94 | 95 | [6] Q. Qiang, B. Zhang, F. Wang, and F. Nie, “Fast Multi-view Discrete Clustering with Anchor Graphs,” in _Proc. AAAI Conf. Artif. Intell._, May 2021, pp. 9360–9367. doi: [10.1609/aaai.v35i11.17128](https://doi.org/10.1609/aaai.v35i11.17128). 96 | 97 |
98 | 99 | [7] F. Nie, L. Tian, and X. Li, “Multiview clustering via adaptively weighted procrustes,” in _Proc. ACM Int. Conf. Knowl. Discov. Data Min._, 2018, pp. 2022–2030. doi: [10.1145/3219819.3220049](https://doi.org/10.1145/3219819.3220049). 100 | 101 |
102 | 103 | [8] Z. Zhang, L. Liu, F. Shen, H. T. Shen, and L. Shao, “Binary Multi-View Clustering,” _IEEE Transactions on Pattern Analysis and Machine Intelligence_, vol. 41, no. 7, pp. 1774–1782, Jul. 2019, doi: [10.1109/TPAMI.2018.2847335](https://doi.org/10.1109/TPAMI.2018.2847335). 104 | 105 |
106 | 107 | [9] Y. Li, F. Nie, H. Huang, and J. Huang, “Large-scale multi-view spectral clustering via bipartite graph,” in _Proc. AAAI Conf. Artif. Intell._, 2015. doi: [10.1609/aaai.v29i1.9598](https://doi.org/10.1609/aaai.v29i1.9598). 108 | 109 |
110 | 111 | [10] S. Shi, F. Nie, R. Wang, and X. Li, “Fast Multi-View Clustering via Prototype Graph,” _IEEE Transactions on Knowledge and Data Engineering_, vol. 35, no. 1, pp. 443–455, Jan. 2023, doi: [10.1109/TKDE.2021.3078728](https://doi.org/10.1109/TKDE.2021.3078728). 112 | 113 |
114 | 115 | [11] N. Rasiwasia et al., “A new approach to cross-modal multimedia retrieval,” in _Proc. ACM Int. Conf. Multimedia_, 2010, pp. 251–260. doi: [10.1145/1873951.1873987](https://doi.org/10.1145/1873951.1873987). 116 | 117 | 118 |
119 | 120 | [12] C. H. Lampert, H. Nickisch, and S. Harmeling, “Learning to detect unseen object classes by between-class attribute transfer,” in _Proc. IEEE Conf. Comput. Vis. Pattern Recognit._, Jun. 2009, pp. 951–958. doi: [10.1109/CVPR.2009.5206594](https://doi.org/10.1109/CVPR.2009.5206594). 121 | 122 |
123 | 124 | [13] S.-G. Fang, D. Huang, X.-S. Cai, C.-D. Wang, C. He, and Y. Tang, “Efficient Multi-view Clustering via Unified and Discrete Bipartite Graph Learning,” _IEEE Transactions on Neural Networks and Learning Systems_, pp. 1–12, Apr. 2023, doi: [10.1109/TNNLS.2023.3261460](https://doi.org/10.1109/TNNLS.2023.3261460). 125 | 126 |
127 | 128 | [14] H. Wei, L. Chen, C. L. P. Chen, J. Duan, R. Han, and L. Guo, “Fuzzy clustering for multiview data by combining latent information,” _Applied Soft Computing_, vol. 126, p. 109140, Sep. 2022, doi: [10.1016/j.asoc.2022.109140](https://doi.org/10.1016/j.asoc.2022.109140). 129 | 130 |
131 | 132 | [15] S. Huang, Z. Kang, and Z. Xu, “Auto-weighted multi-view clustering via deep matrix decomposition,” _Pattern Recognition_, vol. 97, p. 107015, Jan. 2020, doi: [10.1016/j.patcog.2019.107015](https://doi.org/10.1016/j.patcog.2019.107015). 133 | 134 |
135 | 136 | [16] J. Yang and C.-T. Lin, “Multi-View Adjacency-Constrained Hierarchical Clustering,” _IEEE Transactions on Emerging Topics in Computational Intelligence_, pp. 1–13, 2022, doi: [10.1109/TETCI.2022.3221491](https://doi.org/10.1109/TETCI.2022.3221491). 137 | 138 |
139 | 140 | [17] J. Wu and J. M. Rehg, “Centrist: A visual descriptor for scene categorization,” _IEEE transactions on pattern analysis and machine intelligence_, vol. 33, no. 8, pp. 1489–1501, 2010. 141 | 142 |
143 | 144 | [18] A. Oliva and A. Torralba, “Modeling the Shape of the Scene: A Holistic Representation of the Spatial Envelope,” _International Journal of Computer Vision_, vol. 42, no. 3, pp. 145–175, May 2001, doi: [10.1023/A:1011139631724](https://doi.org/10.1023/A:1011139631724). 145 | 146 |
147 | 148 | [19] X. Liu _et al._, “Efficient and Effective Regularized Incomplete Multi-View Clustering,” _IEEE Transactions on Pattern Analysis and Machine Intelligence_, vol. 43, no. 8, pp. 2634–2646, Aug. 2021, doi: [10.1109/TPAMI.2020.2974828](https://doi.org/10.1109/TPAMI.2020.2974828). 149 | 150 |
151 | 152 | [20] X. Niu, C. Zhang, X. Zhao, L. Hu, and J. Zhang, “A multi-view ensemble clustering approach using joint affinity matrix,” _Expert Systems with Applications_, vol. 216, p. 119484, Apr. 2023, doi: [10.1016/j.eswa.2022.119484](https://doi.org/10.1016/j.eswa.2022.119484). 153 | 154 |
155 | 156 | [21] W. Xia, Q. Gao, Q. Wang, X. Gao, C. Ding, and D. Tao, “Tensorized Bipartite Graph Learning for Multi-View Clustering,” _IEEE Transactions on Pattern Analysis and Machine Intelligence_, vol. 45, no. 4, pp. 5187–5202, Apr. 2023, doi: [10.1109/TPAMI.2022.3187976](https://doi.org/10.1109/TPAMI.2022.3187976). 157 | 158 |
159 | 160 | [22] X. Xie and Y. Xiong, “Generalized multi-view learning based on generalized eigenvalues proximal support vector machines,” _Expert Systems with Applications_, vol. 194, p. 116491, May 2022, doi: [10.1016/j.eswa.2021.116491](https://doi.org/10.1016/j.eswa.2021.116491). 161 | 162 |
163 | 164 | [23] S. Luo, C. Zhang, W. Zhang, and X. Cao, “Consistent and Specific Multi-View Subspace Clustering,” in _Proc. AAAI Conf. Artif. Intell._, Apr. 2018. doi: [10.1609/aaai.v32i1.11617](https://doi.org/10.1609/aaai.v32i1.11617). 165 | 166 |
167 | 168 | [24] J. Wu, Z. Lin, and H. Zha, “Essential Tensor Learning for Multi-View Spectral Clustering,” _IEEE Transactions on Image Processing_, vol. 28, no. 12, pp. 5910–5922, Feb. 2019, doi: [10.1109/TIP.2019.2916740](https://doi.org/10.1109/TIP.2019.2916740). 169 | 170 |
171 | 172 | [25] G.-Y. Zhang, D. Huang, and C.-D. Wang, “Facilitated low-rank multi-view subspace clustering,” _Knowledge-Based Systems_, vol. 260, p. 110141, Jan. 2023, doi: [10.1016/j.knosys.2022.110141](https://doi.org/10.1016/j.knosys.2022.110141). 173 | 174 | -------------------------------------------------------------------------------- /Reuters-1200.mat: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ChuanbinZhang/Multi-view-datasets/03ead31e6538322e4e0701c3c28a01595c1a015d/Reuters-1200.mat -------------------------------------------------------------------------------- /Reuters-1500.mat: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ChuanbinZhang/Multi-view-datasets/03ead31e6538322e4e0701c3c28a01595c1a015d/Reuters-1500.mat -------------------------------------------------------------------------------- /UCI.mat: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ChuanbinZhang/Multi-view-datasets/03ead31e6538322e4e0701c3c28a01595c1a015d/UCI.mat -------------------------------------------------------------------------------- /WebKB.mat: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ChuanbinZhang/Multi-view-datasets/03ead31e6538322e4e0701c3c28a01595c1a015d/WebKB.mat -------------------------------------------------------------------------------- /Wikipedia-test.mat: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ChuanbinZhang/Multi-view-datasets/03ead31e6538322e4e0701c3c28a01595c1a015d/Wikipedia-test.mat -------------------------------------------------------------------------------- /Wikipedia.mat: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ChuanbinZhang/Multi-view-datasets/03ead31e6538322e4e0701c3c28a01595c1a015d/Wikipedia.mat -------------------------------------------------------------------------------- /Yale.mat: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ChuanbinZhang/Multi-view-datasets/03ead31e6538322e4e0701c3c28a01595c1a015d/Yale.mat --------------------------------------------------------------------------------