**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 linear discriminant analysis (LDA) using the zero-norm. The resulting optimization problem is non-convex, discontinuous and very hard to solve. We overcome the discontinuity by using an appropriate continuous approximation to zero-norm such that the resulting problem can be formulated as a DC (Difference of Convex functions) program to which DC programming and DC Algorithms (DCA) can be investigated. The computational results show the efficiency and the superiority of our approach versus the l 1 regularization model on both feature selection and classification.

Keywords: Classification, Sparse Fisher linear discriminant analysis, DC programming, DCA.

**Citation:** Phan Duy Nhat, Nguyen Manh Cuong Nguyen, Le Thi Hoai An: A DC Programming Approach for Sparse Linear Discriminant Analysis. Advances in Intelligent Systems and Computing ISBN 978-3-319-06568-7, pp. 65-74, Springer 2014.

Springer 2014