Sparsity Score: A new filter feature selection method based on graph

Recently, l
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graph based analysis using sparse representation has received much attention in pattern recognition and related communities. In this paper, motivated by the success of l
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graph in dimensionality reduction, we extend it to feature selection and propose a novel filter-type method called Sparsity Score (SS) which ranks features according to their respective sparsity preserving capability. For that aim, a l
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graph is constructed based on sparse representationon samples, where a l
₁
-norm based optimization is used to simultaneously determine the graph adjacency structure and corresponding graph weights of the l
₁
graph. Experimental results on a series of benchmark data sets show that the proposed SS method achieves better performance than conventional feature selection methods.