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.
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Nhat, P.D., Nguyen, M.C., Le Thi, H.A. (2014). A DC Programming Approach for Sparse Linear Discriminant Analysis. In: van Do, T., Thi, H., Nguyen, N. (eds) Advanced Computational Methods for Knowledge Engineering. Advances in Intelligent Systems and Computing, vol 282. Springer, Cham. https://doi.org/10.1007/978-3-319-06569-4_4
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DOI: https://doi.org/10.1007/978-3-319-06569-4_4
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