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Conference Paper: Sparse kernel canonical correlation analysis
Title | Sparse kernel canonical correlation analysis |
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Authors | |
Keywords | Kernel Sparsity Canonical correlation analysis |
Issue Date | 2013 |
Publisher | Newswood Ltd. The Conference Proceedings' web site is located at http://www.iaeng.org/publication/IMECS2013/ |
Citation | The International MultiConference of Engineers and Computer Scientists (IMECS) 2013, Hong Kong, 13-15 March, 2013. In Lecture Notes in Engineering and Computer Science, 2013, v. 2202, p. 322-327 How to Cite? |
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 l |
Persistent Identifier | http://hdl.handle.net/10722/276485 |
ISBN | |
ISSN | 2020 SCImago Journal Rankings: 0.117 |
DC Field | Value | Language |
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dc.contributor.author | Chu, Delin | - |
dc.contributor.author | Liao, Li Zhi | - |
dc.contributor.author | Ng, Michael K. | - |
dc.contributor.author | Zhang, Xiaowei | - |
dc.date.accessioned | 2019-09-18T08:33:44Z | - |
dc.date.available | 2019-09-18T08:33:44Z | - |
dc.date.issued | 2013 | - |
dc.identifier.citation | The International MultiConference of Engineers and Computer Scientists (IMECS) 2013, Hong Kong, 13-15 March, 2013. In Lecture Notes in Engineering and Computer Science, 2013, v. 2202, p. 322-327 | - |
dc.identifier.isbn | 978-988-19251-8-3 | - |
dc.identifier.issn | 2078-0958 | - |
dc.identifier.uri | http://hdl.handle.net/10722/276485 | - |
dc.description.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 l<inf>1</inf>-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. | - |
dc.language | eng | - |
dc.publisher | Newswood Ltd. The Conference Proceedings' web site is located at http://www.iaeng.org/publication/IMECS2013/ | - |
dc.relation.ispartof | Lecture Notes in Engineering and Computer Science | - |
dc.subject | Kernel | - |
dc.subject | Sparsity | - |
dc.subject | Canonical correlation analysis | - |
dc.title | Sparse kernel canonical correlation analysis | - |
dc.type | Conference_Paper | - |
dc.description.nature | link_to_OA_fulltext | - |
dc.identifier.scopus | eid_2-s2.0-84880075803 | - |
dc.identifier.volume | 2202 | - |
dc.identifier.spage | 322 | - |
dc.identifier.epage | 327 | - |
dc.identifier.issnl | 2078-0958 | - |