Hoai An Le Thi, Duy Nhat Phan, DC Programming and DCA for Sparse Fisher Linear Discriminant Analysis.

 

Abstract: we consider the supervised pattern classification in the high-dimensional setting, in which the number of features is much larger than the number of observations. We present a novel approach to the sparse Fisher linear discriminant problem using the l0-norm. The resulting optimization problem is nonconvex, discontinuous and very hard to solve. We overcome the discontinuity by using appropriate approximations to the l0-norm such that the resulting problems can be formulated as difference of convex functions (DC) programs to which DC programming and DC Algorithms (DCA) are investigated. The experimental results on both simulated and real datasets demonstrate the efficiency of the proposed algorithms compared to some state-of-the-art methods.

 

Keywords: Classification Feature selection Fisher linear discriminant analysis DC programming DCA.

 

Citation: Hoai An Le Thi, Duy Nhat Phan, DC Programming and DCA for Sparse Fisher Linear Discriminant Analysis,  to appear in Neural Computing and Applications.

 

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