Sparse Representation Preserving for Unsupervised Feature Selection

Recent research has demonstrated that sparse coding (or sparse representation) is a powerful tool for pattern classification. This paper presents a new unsupervised feature selection method, termed Sparse Representation Preserving Feature Selection (SRPFS), which aims at minimizing reconstruction residual based on sparse representation in the subspace of the selected features. A greedy algorithm and a joint selection algorithm are devised to efficiently solve the proposed combinatorial optimization formulation. In particular, the latter algorithm incorporates both l_2,1 -norm and l₁-norm minimization within unsupervised feature selection framework. The experimental results on four real-world datasets demonstrate the improvements brought by our proposed SRPFS with joint selection algorithm.