Optimal Feature Selection for Robust Classification via l2,1-Norms Regularization
Jiajun Wen, Zhihui Lai, Wai Keung Wong, Jinrong Cui, Minghua Wan · 2014
This paper aims to explore the optimal feature selection with dimensionality reduction and jointly sparse representation scheme for classification. The proposed method is called Optimal Feature Selection Classification (OFSC). Our model simultaneously learns an orthogonal subspace for jointly sparse feature selection and representation via l2,1-norms regularization. To solve the proposed model, an alternately iterative algorithm is proposed to optimize both the jointly sparse projection matrix and representation matrix. Experimental results on three public face datasets and one action dataset validate the quick convergence of our algorithm and show that the proposed method is more competitive than the state-of-the-art methods.