Instance Selection Using Nonlinear Sparse Modeling.

Sparse modeling representative selection (SMRS) has been recently introduced for selecting the most relevant examples in data sets. SMRS exploits data self-representativeness coding in order to infer a coding matrix with block sparsity constraint. The relevance scores of samples are then derived from the estimated matrix of coefficients. Since SMRS is based on a linear model for data self-representation, it cannot always provide good relevant samples. Besides, most of its selected samples can be found in dense areas in input space. In this paper, we propose to overcome the SMRS method’s shortcomings that are related to the coding matrix estimation. We introduce two nonlinear data self-representativeness coding schemes that are based on Hilbert space and column generation. Experimental evaluation is carried out on summarizing a video movie and on summarizing training image data sets used for classification tasks. These experiments demonstrated that the proposed nonlinear methods can outperform state-of-the art selection methods. [ABSTRACT FROM AUTHOR]