Unsupervised Feature Selection with Graph Regularized Nonnegative Self-representation

In this paper, we propose a novel algorithm called Graph Regularized Nonnegative Self Representation (GRNSR) for unsupervised feature selection. In our proposed GRNSR, each feature is first represented as a linear combination of its relevant features. Then, an affinity graph is constructed based on nonnegative least squares to capture the inherent local structure information of data. Finally, the l 2,1-norm and nonnegative constraint are imposed on the representation coefficient matrix to achieve feature selection in batch mode. Moreover, we develop a simple yet efficient iterative update algorithm to solve GRNSR. Extensive experiments are conducted on three publicly available databases (Extended YaleB, CMU PIE and AR) to demonstrate the efficiency of the proposed algorithm. Experimental results show that GRNSR obtains better recognition performance than some other state-of-the-art approaches.

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