文献类型：期刊文献

英文题名：Collaborative and geometric multi-kernel learning for multi-class classification

作者：Wang, Zhe[1];Zhu, Zonghai[1];Li, Dongdong[1]

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

年份：2020

卷号：99

外文期刊名：PATTERN RECOGNITION

收录：;EI(收录号：20194407610348);WOS:【SCI-EXPANDED(收录号:WOS:000504503500007)】；

基金：This work is supported by "Shuguang Program" supported by Shanghai Education Development Foundation and Shanghai Municipal Education Commission, Natural Science Foundation of China under Grant no. 61672227, Natural Science Foundations of China under Grant no. 61806078, National Key R&D Program of China under Grant no. 2018YFC0910500, the Special Fund Project for Shanghai Informatization Development in Big Data under Grant no. 201901043, and National Major Scientific and Technological Special Project for "Significant New Drugs Development" under Grant no. 2019ZX09201004.

语种：英文

外文关键词：Multi-class classification; Empirical kernel mapping; Multiple empirical kernel learning; Regularized learning

摘要：The multi-class classification is the problem of classifying the sample into one of three or more classes. In this paper, we propose an algorithm named collaborative and geometric multi-kernel learning (CGMKL) to classify multi-class data into corresponding class directly. The CGMKL uses the Multiple Empirical Kernel Learning (MEKL) to map the sample into multiple kernel spaces, and then trains the softmax function in each kernel space. To realize the collaborative learning, one regularization term, which controls the consistent outputs of samples in different kernel spaces, provides the complementary information. Moreover, another regularization term exhibits the classification result with a geometric feature by reducing the within-class distance of the outputs of samples. Extensive Experiments on the multi-class data sets validate the effectiveness of the CGMKL. (C) 2019 Elsevier Ltd. All rights reserved.