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Article: Sparse orthogonal linear discriminant analysis
Title | Sparse orthogonal linear discriminant analysis |
---|---|
Authors | |
Keywords | Sparsity Linear discriminant analysis Dimensionality reduction |
Issue Date | 2012 |
Citation | SIAM Journal on Scientific Computing, 2012, v. 34, n. 5, p. A2421-A2443 How to Cite? |
Abstract | In this paper, sparse orthogonal linear discriminant analysis (OLDA) is studied. The main contributions of the present work include the following: (i) all minimum Frobeniusnorm/dimension solutions of the optimization problem used for establishing OLDA are characterized explicitly; and (ii) this explicit characterization leads to two numerical algorithms for computing a sparse linear transformation for OLDA. The first is based on the gradient flow approach while the second is a sequential linear Bregman method. We experiment with real world datasets to illustrate that the sequential linear Bregman method is much better than the gradient flow approach. The sequential linear Bregman method always achieves comparable classification accuracy with the normal OLDA, satisfactory sparsity and orthogonality, and acceptable CPU times. © 2012 Society for Industrial and Applied Mathematics. |
Persistent Identifier | http://hdl.handle.net/10722/276942 |
ISSN | 2023 Impact Factor: 3.0 2023 SCImago Journal Rankings: 1.803 |
ISI Accession Number ID |
DC Field | Value | Language |
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dc.contributor.author | Chut, Delin | - |
dc.contributor.author | Liao, Li Zhi | - |
dc.contributor.author | Ng, Michael K. | - |
dc.date.accessioned | 2019-09-18T08:35:07Z | - |
dc.date.available | 2019-09-18T08:35:07Z | - |
dc.date.issued | 2012 | - |
dc.identifier.citation | SIAM Journal on Scientific Computing, 2012, v. 34, n. 5, p. A2421-A2443 | - |
dc.identifier.issn | 1064-8275 | - |
dc.identifier.uri | http://hdl.handle.net/10722/276942 | - |
dc.description.abstract | In this paper, sparse orthogonal linear discriminant analysis (OLDA) is studied. The main contributions of the present work include the following: (i) all minimum Frobeniusnorm/dimension solutions of the optimization problem used for establishing OLDA are characterized explicitly; and (ii) this explicit characterization leads to two numerical algorithms for computing a sparse linear transformation for OLDA. The first is based on the gradient flow approach while the second is a sequential linear Bregman method. We experiment with real world datasets to illustrate that the sequential linear Bregman method is much better than the gradient flow approach. The sequential linear Bregman method always achieves comparable classification accuracy with the normal OLDA, satisfactory sparsity and orthogonality, and acceptable CPU times. © 2012 Society for Industrial and Applied Mathematics. | - |
dc.language | eng | - |
dc.relation.ispartof | SIAM Journal on Scientific Computing | - |
dc.subject | Sparsity | - |
dc.subject | Linear discriminant analysis | - |
dc.subject | Dimensionality reduction | - |
dc.title | Sparse orthogonal linear discriminant analysis | - |
dc.type | Article | - |
dc.description.nature | link_to_subscribed_fulltext | - |
dc.identifier.doi | 10.1137/110851377 | - |
dc.identifier.scopus | eid_2-s2.0-84869819999 | - |
dc.identifier.volume | 34 | - |
dc.identifier.issue | 5 | - |
dc.identifier.spage | A2421 | - |
dc.identifier.epage | A2443 | - |
dc.identifier.eissn | 1095-7200 | - |
dc.identifier.isi | WOS:000310580800001 | - |