Sparse kernel canonical correlation analysis

Delin Chu, Lizhi LIAO, Kwok Po NG, Xiaowei Zhang

Research output: Chapter in book/report/conference proceedingConference contributionpeer-review

12 Citations (Scopus)

Abstract

Canonical correlation analysis (CCA) is a multivariate statistical technique for finding the linear relationship between two sets of variables. The kernel generalization of CCA named kernel CCA has been proposed to find nonlinear relations between data sets. Despite the wide usage of CCA and kernel CCA, they have one common limitation that is the lack of sparsity in their solution. In this paper, we consider sparse kernel CCA and propose a novel sparse kernel CCA algorithm (SKCCA). Our algorithm is based on a relationship between kernel CCA and least squares. Sparsity of the dual transformations is introduced by penalizing the l1-norm of dual vectors. Experiments demonstrate that our algorithm not only performs well in computing sparse dual transformations but also can alleviate the over-fitting problem of kernel CCA.

Original languageEnglish
Title of host publicationProceedings of the International MultiConference of Engineers and Computer Scientists 2013, IMECS 2013
PublisherNewswood Limited
Pages322-327
Number of pages6
ISBN (Print)9789881925183
Publication statusPublished - 2013
EventInternational MultiConference of Engineers and Computer Scientists 2013, IMECS 2013 - Kowloon, Hong Kong
Duration: 13 Mar 201315 Mar 2013

Publication series

NameLecture Notes in Engineering and Computer Science
Volume2202
ISSN (Print)2078-0958

Conference

ConferenceInternational MultiConference of Engineers and Computer Scientists 2013, IMECS 2013
Country/TerritoryHong Kong
CityKowloon
Period13/03/1315/03/13

Scopus Subject Areas

  • Computer Science (miscellaneous)

User-Defined Keywords

  • Canonical correlation analysis
  • Kernel
  • Sparsity

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