Generalized Multiple Kernel Learning With Data-Dependent Priors

doi:10.1109/TNNLS.2014.2334137

	Generalized Multiple Kernel Learning With Data-Dependent Priors
	Qi Mao 1; Ivor W. Tsang 2; Shenghua Gao3 ; Li Wang 4
	2015-06
发表期刊	IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS (IF:10.2[JCR-2023],10.4[5-Year])
ISSN	2162-237X
卷号	26 期号:6 页码:1134-1148
发表状态	已发表
DOI	10.1109/TNNLS.2014.2334137
摘要	Multiple kernel learning (MKL) and classifier ensemble are two mainstream methods for solving learning problems in which some sets of features/views are more informative than others, or the features/views within a given set are inconsistent. In this paper, we first present a novel probabilistic interpretation of MKL such that maximum entropy discrimination with a noninformative prior over multiple views is equivalent to the formulation of MKL. Instead of using the noninformative prior, we introduce a novel data-dependent prior based on an ensemble of kernel predictors, which enhances the prediction performance of MKL by leveraging the merits of the classifier ensemble. With the proposed probabilistic framework of MKL, we propose a hierarchical Bayesian model to learn the proposed data-dependent prior and classification model simultaneously. The resultant problem is convex and other information (e.g., instances with either missing views or missing labels) can be seamlessly incorporated into the data-dependent priors. Furthermore, a variety of existing MKL models can be recovered under the proposed MKL framework and can be readily extended to incorporate these priors. Extensive experiments demonstrate the benefits of our proposed framework in supervised and semisupervised settings, as well as in tasks with partial correspondence among multiple views.
关键词	Data fusion dirty data missing views multiple kernel learning partial correspondence semisupervised learning
URL	查看原文
收录类别	SCI ; EI
语种	英语
资助项目	Australian Research Council[FT130100746]
WOS研究方向	Computer Science ; Engineering
WOS类目	Computer Science, Artificial Intelligence ; Computer Science, Hardware & Architecture ; Computer Science, Theory & Methods ; Engineering, Electrical & Electronic
WOS记录号	WOS:000354957000002
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
EI入藏号	20152200894306
EI主题词	Bayesian networks
EI分类号	Data Processing and Image Processing:723.2 ; Combinatorial Mathematics, Includes Graph Theory, Set Theory:921.4
WOS关键词	PREDICTION
原始文献类型	Article
来源库	IEEE
引用统计	正在获取...
文献类型	期刊论文
条目标识符	https://kms.shanghaitech.edu.cn/handle/2MSLDSTB/2206
专题	信息科学与技术学院_PI研究组_高盛华组
作者单位	1.Department of Electrical and Computer Engineering, Duke University, Durham, NC, USA 2.Centre for Quantum Computation and Intelligent Systems, University of Technology Sydney, Ultimo, NSW, Australia 3.ShanghaiTech University, Shanghai, China 4.Department of Mathematics, University of California at San Diego, La Jolla, CA, USA
推荐引用方式 GB/T 7714	Qi Mao,Ivor W. Tsang,Shenghua Gao,et al. Generalized Multiple Kernel Learning With Data-Dependent Priors[J]. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS,2015,26(6):1134-1148.
APA	Qi Mao,Ivor W. Tsang,Shenghua Gao,&Li Wang.(2015).Generalized Multiple Kernel Learning With Data-Dependent Priors.IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS,26(6),1134-1148.
MLA	Qi Mao,et al."Generalized Multiple Kernel Learning With Data-Dependent Priors".IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 26.6(2015):1134-1148.