Dimensionality reduction by mixed kernel canonical correlation analysis

Zhu, Xiaofeng, Huang, Zi, Shen, Heng Tao, Cheng, Jian and Xu, Changsheng (2012) Dimensionality reduction by mixed kernel canonical correlation analysis. Pattern Recognition, 45 8: 3003-3016. doi:10.1016/j.patcog.2012.02.007

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Author Zhu, Xiaofeng
Huang, Zi
Shen, Heng Tao
Cheng, Jian
Xu, Changsheng
Title Dimensionality reduction by mixed kernel canonical correlation analysis
Journal name Pattern Recognition   Check publisher's open access policy
ISSN 0031-3203
1873-5142
Publication date 2012-08-01
Sub-type Critical review of research, literature review, critical commentary
DOI 10.1016/j.patcog.2012.02.007
Open Access Status Not yet assessed
Volume 45
Issue 8
Start page 3003
End page 3016
Total pages 14
Place of publication Kidlington, United Kingdom
Publisher Pergamon
Language eng
Subject 1712 Software
1711 Signal Processing
1707 Computer Vision and Pattern Recognition
1702 Artificial Intelligence
Abstract 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.
Keyword Dimensionality reduction
Mixed kernel
Canonical Correlation Analysis
Model selection
Q-Index Code C1
Q-Index Status Confirmed Code
Institutional Status UQ
Additional Notes Available online 22 February 2012

Document type: Journal Article
Sub-type: Critical review of research, literature review, critical commentary
Collections: Official 2013 Collection
School of Information Technology and Electrical Engineering Publications
 
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Created: Tue, 06 Mar 2012, 20:56:45 EST by Dr Heng Tao Shen on behalf of School of Information Technol and Elec Engineering