文献类型：期刊文献

英文题名：Globalized and localized matrix-pattern-oriented classification machine

作者：Wang, Zhe[1];Zhu, Yujin[1];Gao, Daqi[1];Guo, Weibin[1]

机构：[1]E China Univ Sci & Technol, Dept Comp Sci & Engn, Shanghai 200237, Peoples R China

年份：2014

卷号：25

起止页码：379

外文期刊名：APPLIED SOFT COMPUTING

收录：;EI(收录号：20144500169811);WOS:【SCI-EXPANDED(收录号:WOS:000344460600032)】；

基金：This work was partially supported by Natural Science Foundations of China under Grant Nos. 61272198 and 21176077, Innovation Program of Shanghai Municipal Education Commission under Grant No. 14ZZ054, the Fundamental Research Funds for the Central Universities, Shanghai Key Laboratory of Intelligent Information Processing of China under Grant No. IIPL-2012-003, and Provincial Key Laboratory for Computer Information Processing Technology of Soochow University

语种：英文

外文关键词：Structural information; Matrix pattern; Regularization learning; Rademacher complexity analysis; Ho-Kashyap algorithm; Pattern classification

摘要：Inspired by the matrix-based methods used in feature extraction and selection, one matrix-pattern-oriented classification framework has been designed in our previous work and demonstrated to utilize one matrix pattern itself more effectively to improve the classification performance in practice. However, this matrix-based framework neglects the prior structural information of the whole input space that is made up of all the matrix patterns. This paper aims to overcome such flaw through taking advantage of one structure learning method named Alternative Robust Local Embedding (ARLE). As a result, a new regularization term R-gl is designed, expected to simultaneously represent the globality and the locality of the whole data domain, further boosting the existing matrix-based classification method. To our knowledge, it is the first trial to introduce both the globality and the locality of the whole data space into the matrixized classifier design. In order to validate the proposed approach, the designed Rgi is applied into the previous work matrix-pattern-oriented Ho-Kashyap classifier (MatMHKS) to construct a new globalized and localized MatMHKS named GLMatMHKS. The experimental results on a broad range of data validate that GLMatMHKS not only inherits the advantages of the matrixized learning, but also uses the prior structural information more reasonably to guide the classification machine design. (C) 2014 Elsevier B.V. All rights reserved.