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Dimensionality reduction by Mixed Kernel Canonical Correlation Analysis

文献类型：期刊论文

作者Zhu, Xiaofeng1; Huang, Zi1; Shen, Heng Tao1; Cheng, Jian; Xu, Changsheng2
刊名PATTERN RECOGNITION
出版日期2012-08-01
卷号45期号:8页码:3003-3016
关键词Dimensionality reduction Mixed kernel Canonical Correlation Analysis Model selection
英文摘要In this paper, we propose a novel method named Mixed Kernel CCA (MKCCA) to achieve easy yet accurate implementation of dimensionality reduction. MKCCA consists of two major steps. First, the high dimensional data space is mapped into the reproducing kernel Hilbert space (RKHS) rather than the Hilbert space, with a mixture of kernels, i.e. a linear combination between a local kernel and a global kernel. Meanwhile, a uniform design for experiments with mixtures is also introduced for model selection. Second, in the new RKHS, Kernel CCA is further improved by performing Principal Component Analysis (PCA) followed by CCA for effective dimensionality reduction. We prove that MKCCA can actually be decomposed into two separate components, i.e. PCA and CCA, which can be used to better remove noises and tackle the issue of trivial learning existing in CCA or traditional Kernel CCA. After this, the proposed MKCCA can be implemented in multiple types of learning, such as multi-view learning, supervised learning, semi-supervised learning, and transfer learning, with the reduced data. We show its superiority over existing methods in different types of learning by extensive experimental results. (C) 2012 Elsevier Ltd. All rights reserved.
WOS标题词Science & Technology ; Technology
类目[WOS]Computer Science, Artificial Intelligence ; Engineering, Electrical & Electronic
研究领域[WOS]Computer Science ; Engineering
关键词[WOS]INDEPENDENCE ; RELEVANCE ; SETS
收录类别SCI
语种英语
WOS记录号WOS:000303294500012
源URL[http://ir.ia.ac.cn/handle/173211/2879]  
专题自动化研究所_模式识别国家重点实验室_多媒体计算与图形学团队
作者单位1.Univ Queensland, Sch Informat Technol & Elect Engn, Brisbane, Qld 4072, Australia
2.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100864, Peoples R China
推荐引用方式
GB/T 7714		 Zhu, Xiaofeng,Huang, Zi,Shen, Heng Tao,et al. Dimensionality reduction by Mixed Kernel Canonical Correlation Analysis[J]. PATTERN RECOGNITION,2012,45(8):3003-3016.
APA Zhu, Xiaofeng,Huang, Zi,Shen, Heng Tao,Cheng, Jian,&Xu, Changsheng.(2012).Dimensionality reduction by Mixed Kernel Canonical Correlation Analysis.PATTERN RECOGNITION,45(8),3003-3016.
MLA Zhu, Xiaofeng,et al."Dimensionality reduction by Mixed Kernel Canonical Correlation Analysis".PATTERN RECOGNITION 45.8(2012):3003-3016.

入库方式： OAI收割

来源：自动化研究所

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