File Download
There are no files associated with this item.
Links for fulltext
(May Require Subscription)
- Publisher Website: 10.1109/TPAMI.2013.104
- Scopus: eid_2-s2.0-84887601329
- WOS: WOS:000326502200019
- Find via
Supplementary
- Citations:
- Appears in Collections:
Article: Sparse canonical correlation analysis: New formulation and algorithm
Title | Sparse canonical correlation analysis: New formulation and algorithm |
---|---|
Authors | |
Keywords | Sparsity orthogonality multivariate data linear discriminant analysis canonical correlation analysis |
Issue Date | 2013 |
Citation | IEEE Transactions on Pattern Analysis and Machine Intelligence, 2013, v. 35, n. 12, p. 3050-3065 How to Cite? |
Abstract | In this paper, we study canonical correlation analysis (CCA), which is a powerful tool in multivariate data analysis for finding the correlation between two sets of multidimensional variables. The main contributions of the paper are: 1) to reveal the equivalent relationship between a recursive formula and a trace formula for the multiple CCA problem, 2) to obtain the explicit characterization for all solutions of the multiple CCA problem even when the corresponding covariance matrices are singular, 3) to develop a new sparse CCA algorithm, and 4) to establish the equivalent relationship between the uncorrelated linear discriminant analysis and the CCA problem. We test several simulated and real-world datasets in gene classification and cross-language document retrieval to demonstrate the effectiveness of the proposed algorithm. The performance of the proposed method is competitive with the state-of-the-art sparse CCA algorithms. © 2013 IEEE. |
Persistent Identifier | http://hdl.handle.net/10722/276969 |
ISSN | 2023 Impact Factor: 20.8 2023 SCImago Journal Rankings: 6.158 |
ISI Accession Number ID |
DC Field | Value | Language |
---|---|---|
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 | 2019-09-18T08:35:12Z | - |
dc.date.available | 2019-09-18T08:35:12Z | - |
dc.date.issued | 2013 | - |
dc.identifier.citation | IEEE Transactions on Pattern Analysis and Machine Intelligence, 2013, v. 35, n. 12, p. 3050-3065 | - |
dc.identifier.issn | 0162-8828 | - |
dc.identifier.uri | http://hdl.handle.net/10722/276969 | - |
dc.description.abstract | In this paper, we study canonical correlation analysis (CCA), which is a powerful tool in multivariate data analysis for finding the correlation between two sets of multidimensional variables. The main contributions of the paper are: 1) to reveal the equivalent relationship between a recursive formula and a trace formula for the multiple CCA problem, 2) to obtain the explicit characterization for all solutions of the multiple CCA problem even when the corresponding covariance matrices are singular, 3) to develop a new sparse CCA algorithm, and 4) to establish the equivalent relationship between the uncorrelated linear discriminant analysis and the CCA problem. We test several simulated and real-world datasets in gene classification and cross-language document retrieval to demonstrate the effectiveness of the proposed algorithm. The performance of the proposed method is competitive with the state-of-the-art sparse CCA algorithms. © 2013 IEEE. | - |
dc.language | eng | - |
dc.relation.ispartof | IEEE Transactions on Pattern Analysis and Machine Intelligence | - |
dc.subject | Sparsity | - |
dc.subject | orthogonality | - |
dc.subject | multivariate data | - |
dc.subject | linear discriminant analysis | - |
dc.subject | canonical correlation analysis | - |
dc.title | Sparse canonical correlation analysis: New formulation and algorithm | - |
dc.type | Article | - |
dc.description.nature | link_to_subscribed_fulltext | - |
dc.identifier.doi | 10.1109/TPAMI.2013.104 | - |
dc.identifier.scopus | eid_2-s2.0-84887601329 | - |
dc.identifier.volume | 35 | - |
dc.identifier.issue | 12 | - |
dc.identifier.spage | 3050 | - |
dc.identifier.epage | 3065 | - |
dc.identifier.isi | WOS:000326502200019 | - |
dc.identifier.issnl | 0162-8828 | - |