Efficient and Robust Feature Selection via Joint L21-Norms Minimization

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Authors Feiping Nie, Heng Huang, Xiao Cai, Chris Ding
Journal/Conference Name Advances in Neural Information Processing Systems 23 (NIPS)
Paper Category
Paper Abstract Feature selection is an important component of many machine learning applications. Especially in many bioinformatics tasks, efficient and robust feature selection methods are desired to extract meaningful features and eliminate noisy ones. In this paper, we propose a new robust feature selection method with emphasizing joint ℓ2,1-norm minimization on both loss function and regularization. The ℓ2,1-norm based loss function is robust to outliers in data points and the ℓ2,1-norm regularization selects features across all data points with joint sparsity. An efficient algorithm is introduced with proved convergence. Our regression based objective makes the feature selection process more efficient. Our method has been applied into both genomic and proteomic biomarkers discovery. Extensive empirical studies are performed on six data sets to demonstrate the performance of our feature selection method.
Date of publication 2010
Code Programming Language MATLAB

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