Dimensionality reduction by Mixed Kernel Canonical Correlation Analysis

详细信息	查看全文 | 推荐本文 |

• 作者：Xiaofeng Zhu^a ; ^{zhux@itee.uq.edu.au}  ; [Author Vitae] ; Zi Huang^a ; ^{huang@itee.uq.edu.au}  ; [Author Vitae] ; Heng Tao Shen^a ; ^{shenht@itee.uq.edu.au}  ; [Author Vitae] ; Jian Cheng^b ; ^{jcheng@nlpr.ia.ac.cn}  ; [Author Vitae] ; Changsheng Xu^b ; ^{csxu@nlpr.ia.ac.cn}  ; [Author Vitae]
• 关键词：Dimensionality reduction ; Mixed kernel ; Canonical Correlation Analysis ; Model selection
• 刊名：Pattern Recognition
• 出版年：2012
• 期刊代码：100_00313203
• 类别：cp
• 出版时间：August, 2012
• 卷：45
• 期：8
• 页码：3003-3016
• 文件大小：502 K

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.