文献类型：期刊文献

英文题名：MREKLM: A fast multiple empirical kernel learning machine

作者：Fan, Qi[1];Wang, Zhe[1,3];Zha, Hongyuan[2];Gao, Daqi[1]

机构：[1]East China Univ Sci & Technol, Dept Comp Sci & Engn, Shanghai 200237, Peoples R China;[2]Georgia Inst Technol, Coll Comp, Sch Computat Sci & Engn, Atlanta, GA 30332 USA;[3]Soochow Univ, Prov Key Lab Comp Informat Proc Technol, Suzhou 215006, Peoples R China

年份：2017

卷号：61

起止页码：197

外文期刊名：PATTERN RECOGNITION

收录：;EI(收录号：20163902846651);WOS:【SCI-EXPANDED(收录号:WOS:000385899400014)】；

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 under Grant No. KJS1323, and NSF CNS-1505790.

语种：英文

外文关键词：Multiple Kernel Learning; Empirical Kernel Mapping; Random projection; Analytical optimization; Classifier design; Pattern recognition

摘要：Multiple Empirical Kernel Learning (MEKL) explicitly maps samples into different empirical feature spaces in which the kernel features of the mapped samples can be directly provided. Thus, MEKL is much easier than the conventional Multiple Kernel Learning (MKL) in terms of processing and analyzing the structure of mapped feature spaces. However, the computational complexity of MEKL with M empirical feature spaces is O (MN3) where N is the number of training samples. The dimensions of the generated empirical feature spaces are approximate to N. When dealing with large-scale problems, MEKL cannot handle them properly due to the severe computation and memory burden. Moreover, most existing MEKL utilizes the gradient decent optimization to learn classifiers, but it is time consuming for training. Therefore, this paper proposes a Multiple Random Empirical Kernel Learning Machine (MREKLM) to overcome these problems. The proposed MREKLM adopts the random projection idea to map samples into multiple low-dimensional empirical feature spaces with lower computational complexity O (MP3), where P(<< N) is the number of the randomly selected samples. After that, MREKLM adopts an analytical optimization approach to directly deal with multi-class problems. The computational complexity of MREKLM is O ((MP3)-P-3). Experimental results also validate both efficiency and effectiveness of the proposed MREKLM. The contributions of this work are: (1) proposing a fast MEKL algorithm named MREKLM, (2) introducing an efficient random empirical kernel mapping approach, and (3) extending the capability of MEKL to handle large-scale problems. (C) 2016 Elsevier Ltd. All rights reserved.