├── Feature selection based on structured sparsity ├── Discriminative least squares regression for multiclass classification and feature selection │ ├── LEAST_~1.M │ ├── OPTIMI~1.M │ ├── OPTIMI~2.M │ ├── SOLVE_~1.M │ ├── TRAIN_~1.M │ ├── data_XX.mat │ ├── data_Y_id.mat │ ├── demo_for_feature_selection.m │ └── readme_for_feature_selection.txt ├── Efficient and robust feature selection via joint l2,1-norms minimization │ ├── FeatureSelection.m │ ├── L21R21.m │ ├── L21R21_inv.m │ └── readme.txt ├── Exact top-k feature selection via l2,0-norm constraint │ └── FSRobust_ALM.m ├── Joint embedding learning and sparse regression │ ├── compute_W.m │ ├── compute_Y.m │ └── jelsr.m ├── L1 │ ├── ProducePoNe.m │ └── ProducePoNeV1.m ├── L21-Norm Regularized Discriminative Feature Selection for Unsupervised Learning │ ├── LocalDisAna.m │ ├── LquadR21_reg.m │ └── call.m └── l2,1 regularized correntropy for robust feature selection │ ├── CRFS.m │ ├── RFS.m │ ├── SY2MY.m │ └── test.m ├── Multi-View 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