自动化学报2011，Vol.37Issue(6)：693-699,7.DOI:10.3724/SP.J.1004.2011.00693
弹性多核学习
Elastic Multiple Kernel Learning
摘要
Abstract
Multiple kernel learning (MKL) was proposed to deal with kernel fusion. MKL learns a linear combination of several kernels and solves the supporting vector machine (SVM) associated with the combined kernel simultaneously. Current framework of MKL encourages sparsity of the kernel combination coefficients. When a significant portion of the kernels are informative, forcing sparsity tends to select only a few kernels and may ignore useful information. In this paper, we propose elastic multiple kernel learning (EMKL) to achieve adaptive kernel fusion. EMKL makes use of a mixing regularization function to compromise sparsity and non-sparsity. Both MKL and SVM could be regarded as special cases of EMKL. Based on gradient descent algorithm for MKL problem, we propose a fast algorithm to solve EMKL problem. Results on the simulation datasets demonstrate that the performance of EMKL compares favorably to both MKL and SVM. We further apply EMKL to gene set analysis and get promising results.Finally, we study the theoretical advantage of EMKL comparing to other non-sparse MKL.关键词
Support vector machine (SVM)/multiple kernel learning (MKL)/elastic multiple kernel learning (EMKL)/regularizationKey words
Support vector machine (SVM)/multiple kernel learning (MKL)/elastic multiple kernel learning (EMKL)/regularization引用本文复制引用
武征鹏,张学工..弹性多核学习[J].自动化学报,2011,37(6):693-699,7.基金项目
Supported by National Natural Science Foundation of China (61021063) （61021063）
