文献类型：期刊文献

英文题名：Structural multiple empirical kernel learning

作者：Wang, Zhe[1];Fan, Qi[1];Ke, Sheng[1];Gao, Daqi[1]

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

年份：2015

卷号：301

起止页码：124

外文期刊名：INFORMATION SCIENCES

收录：;EI(收录号：20150700532061);WOS:【SCI-EXPANDED(收录号:WOS:000350929100008)】；

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：Multiple kernel learning; Empirical kernel mapping; Structural learning; Cluster information; Rademacher complexity; Pattern recognition

摘要：Multiple Kernel Learning (MKL) can boost classification performance through using multiple kernels rather than a single fixed one. Unlike the traditional MKL with the implicit kernels, Multiple Empirical Kernel Learning (MEKL) explicitly maps input data into multiple feature spaces. This paper focuses on MEKL and proposes an effective Threefold Structural MEKL (TSMEKL). The first fold structure is the space structural information between different mapped feature spaces. The second one is the class discriminant information within each mapped feature space. The third one is the cluster structural information of samples in each mapped feature space. The classical MEKL mainly pays attention to the first two structures, but neglects the last one. The proposed TSMEKL introduces the cluster structural information into MEKL. Doing so can simultaneously utilize the space, the class, and the cluster information in the way from globality to locality. Therefore, TSMEKL utilizes threefold structural information to result in the improvement of classification performance. To the best of our knowledge, it is the first time to introduce the cluster information into the MEKL framework The main advantage of the developed TSMEKL is considering different folds of data information to improve classification performance. The experimental results validate the feasibility and effectiveness of TSMEKL. Moreover, we discuss the theoretical and experimental generalization risk bound of the proposed algorithm in terms of the Rademacher complexity. (c) 2015 Elsevier Inc. All rights reserved.