Nonsmooth optimization approach to data classification

We reduce the supervised classification to solving a nonsmooth optimization problem. The proposed method allows one to solve classification problems for databases with arbitrary number of classes. Numerical experiments have been carried out with databases of small and medium size. We present their results and provide comparison of these results with ones obtained by other algorithms of classification based on the optimization techniques. Results of numerical experiments show effectiveness of the proposed algorithms.