Dimensionality reduction by Mixed Kernel Canonical Correlation Analysis

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 Hilber...

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Bibliographic Details
Published in:Pattern recognition Vol. 45; no. 8; pp. 3003 - 3016
Main Authors: Zhu, Xiaofeng, Huang, Zi, Tao Shen, Heng, Cheng, Jian, Xu, Changsheng
Format: Journal Article
Language:English
Published: Kidlington Elsevier Ltd 01.08.2012
Elsevier
Subjects:
Applied sciences
Artificial intelligence
Canonical Correlation Analysis
Computer science; control theory; systems
Design engineering
Dimensionality reduction
Exact sciences and technology
Hilbert space
Image processing
Information, signal and communications theory
Kernels
Learning
Mixed kernel
Model selection
Noise
Pattern recognition
Pattern recognition. Digital image processing. Computational geometry
Principal component analysis
Reduction
Signal processing
Telecommunications and information theory
Dimensionality reduction
Mixed kernel
Model selection
Canonical Correlation Analysis
Image processing
Canonical correlation
Implementation
Kernel method
Statistical method
Dimension reduction
Multi-view technology
Canonical analysis
Linear combination
Correlation analysis
Supervised learning
Hilbert space
Principal component analysis
ISSN:0031-3203, 1873-5142
Online Access:Get full text
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Summary: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. ► Utilization of a mixture of kernels is more effective for dimensionality reduction. ► In the RKHS, PCA followed by CCA can better remove noises. ► One dimensionality reduction method can be applied for multiple types of learning.
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ISSN:0031-3203
1873-5142
DOI:10.1016/j.patcog.2012.02.007