Multiple rank multi-linear twin support matrix classification machine¹

Article type: Research Article

Authors: Jiang, Rong | Yang, Zhi-Xia^{; *}

Affiliations: College of Mathematics and Systems Science, Xinjiang University, Urumqi, P.R.China

Correspondence: [*] Corresponding author. Zhi-Xia Yang, xjyangzhx@sina.com.

Note: [1] This work is supported by the National Natural Science Foundation of China (No. 11561066).

Abstract: 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.

Keywords: Classification, matrix learning, tensor learning

DOI: 10.3233/JIFS-17414

Journal: Journal of Intelligent & Fuzzy Systems, vol. 35, no. 5, pp. 5741-5754, 2018