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Erschienen in:
2015 | OriginalPaper | Buchkapitel
A DC Programming Approach for Sparse Optimal Scoring
verfasst von : Hoai An Le Thi, Duy Nhat Phan
Erschienen in: Advances in Knowledge Discovery and Data Mining
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We consider the supervised classification problem in the high-dimensional setting. High-dimensionality makes the application of most classification difficult. We present a novel approach to the sparse linear discriminant analysis (LDA) based on its optimal scoring interpretation and the zero-norm. The difficulty in treating the zero-norm is overcome by using an appropriate continuous approximation such that the resulting problem can be formulated as a DC (Difference of Convex functions) program to which DCA (DC Algorithms) is investigated. The computational results on both simulated data and real microarray cancer data show the efficiency of the proposed algorithm in feature selection as well as classification.