Feature selection by combining subspace learning with sparse representation

A novel feature selection algorithm is designed for high-dimensional data classification. The relevant features are selected with the least square loss function and -norm regularization term if the minimum representation error rate between the features and labels is approached with respect to only these features. Taking into account both the local and global structures of data distribution with subspace learning, an efficient optimization algorithm is proposed to solve the joint objective function, so as to select the most representative features and noise-resistant features to enhance the performance of classification. Sets of experiments are conducted on benchmark datasets, show that the proposed approach is more effective and robust than existing feature selection algorithms.