Sparsity preserving score for feature selection
Hui Yan · Applied Informatics · 2015
Compared with supervised feature selection, selecting features in unsupervised learning scenarios is a much harder problem due to the lack of label information. In this paper, we propose sparsity preserving score (SPS) for unsupervised feature selection based on recent advances in sparse representation technique. SPS evaluates the importance of a feature by its power of sparse reconstructive relationship preserving. Specially, SPS selects features that minimize reconstruction residual based on sparse representation in the space of selected features. SPS aims to jointly select features by transforming data from a high-dimensional space of original features to a low-dimensional space of selected features through a special binary feature selection matrix. When the sparse representation is fixed, our searching strategy is an essentially discrete optimization and our theoretical analysis guarantees our objective function can be easily solved with a closed-form solution. The experimental results on two face data sets demonstrate the effectiveness and efficiency of our algorithm.