Sparse Representation-Based Approach for Unsupervised Feature Selection


Dimension reduction methods including feature selection and feature extraction have played an important role in data mining and pattern recognition. In this study, we propose a novel unsupervised feature selection approach based on sparse representation theory, namely Sparsity Score (SS). Due to the sparse representation procedure, SS not only owns the global property of Variance Score (VS) and the local property of Laplacian Score (LS), but also possesses the discriminating nature. Experimental results, based on three well-known face datasets (Yale, ORL and CMU PIE), reveal that SS performs well in the evaluation of the feature signi ̄cance, and it signi ̄cantly outperforms VS and LS.

DOI: 10.1142/S0218001414500062

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@article{Su2014SparseRA, title={Sparse Representation-Based Approach for Unsupervised Feature Selection}, author={Yaru Su and Chuanxi Li and Rujing Wang and Peng Chen}, journal={IJPRAI}, year={2014}, volume={28} }