L 1 Graph Based on Sparse Coding for Feature Selection

In machine learning and pattern recognition, feature selection has been a very active topic in the literature. Unsupervised feature selection is challenging due to the lack of label which would supply the categorical information. How to define an appropriate metric is the key for feature selection. In this paper, we propose a "filter" method for unsupervised feature selection, which is based on the geometry properties of l1 graph. l1 graph is constructed through sparse coding. The graph establishes the relations of feature subspaces and the quality of features is evaluated by features' local preserving ability. We compare our method with classic unsupervised feature selection methods (Laplacian score and Pearson correlation) and supervised method (Fisher score) on benchmark data sets. The classification results based on support vector machine, k-nearest neighbors and multi-layer feed-forward networks demonstrate the efficiency and effectiveness of our method.