Multiple rank multi-linear twin support matrix classification machine

摘要

In this paper, a novel learning frameworks-multiple rank multi-linear twin support matrix classification machine (MRMLTSMCM) is outlined, as an extension of twin support vector machine (TWSVM). Different from TWSVM, MRMLTSMCM uses two pairs of projecting matrixes to construct the pair of functions, which are used to establish decision function. Compared with the vector-based method, the matrix-based could not only keep the structure of the matrix data but also reduce computational complexity. In addition, a regularization term is considered adding to improve the performance of MRMLTSMCM. Moreover, a novel algorithm for MRMLTSMCM is introduced. Finally, experimental results show the effectiveness of the method by classification accuracy, convergence behavior and computation time.