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Semi-supervised discriminant analysis based on UDP regularization
We propose a semi-supervised learning algorithm for discriminant analysis, which uses the geometric structure of both labeled and unlabeled samples and perform a manifold regularization on LDA. The labeled data points provide labeling information and the unlabeled data points provide extra geometric...
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| creator | Huining Qiu Jianhuang Lai Jian Huang Yu Chen |
| description | We propose a semi-supervised learning algorithm for discriminant analysis, which uses the geometric structure of both labeled and unlabeled samples and perform a manifold regularization on LDA. The labeled data points provide labeling information and the unlabeled data points provide extra geometric structure information of the data, then we learn a labeling function which is as smooth as possible on the data manifold. Experiments on several face databases show the effectiveness of the algorithm. |
| doi_str_mv | 10.1109/ICPR.2008.4761802 |
| format | conference_proceeding |
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Experiments on several face databases show the effectiveness of the algorithm.</description><subject>Algorithm design and analysis</subject><subject>Face</subject><subject>Feature extraction</subject><subject>Image databases</subject><subject>Labeling</subject><subject>Linear discriminant analysis</subject><subject>Pattern recognition</subject><subject>Performance analysis</subject><subject>Semisupervised learning</subject><subject>Sun</subject><issn>1051-4651</issn><issn>2831-7475</issn><isbn>9781424421749</isbn><isbn>1424421748</isbn><isbn>9781424421756</isbn><isbn>1424421756</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2008</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><recordid>eNpVkMtKw0AYhccbWGsfQNzkBSbOP9c_yxJvhYJF67rMrTKSpiWTCvXpjdiNq7P4OB-HQ8gNsBKAVXezevFacsawlEYDMn5CJpVBkFxKDkbpUzLiKIAaadTZPyarczICpoBKreCSXOX8yRhnQuGITN_iJtG838XuK-UYipCy79ImtbbtC9va5pBTLpz9Zdu2eL9fFF382De2S9-2T9v2mlysbZPj5Jhjsnx8WNbPdP7yNKunc5oq1lMRtfReeW29iwqEDAaNR2nWwQSpg_chBETnMCpulVFO4MA4D0MD0Ikxuf3TphjjajdMtN1hdTxD_ADADk_l</recordid><startdate>200812</startdate><enddate>200812</enddate><creator>Huining Qiu</creator><creator>Jianhuang Lai</creator><creator>Jian Huang</creator><creator>Yu Chen</creator><general>IEEE</general><scope>6IE</scope><scope>6IL</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIL</scope></search><sort><creationdate>200812</creationdate><title>Semi-supervised discriminant analysis based on UDP regularization</title><author>Huining Qiu ; Jianhuang Lai ; Jian Huang ; Yu Chen</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-i90t-3e64cc5c6acbe5134d787c847fd7d46dccddd88bb8e52a575b387fd22d6ac18b3</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2008</creationdate><topic>Algorithm design and analysis</topic><topic>Face</topic><topic>Feature extraction</topic><topic>Image databases</topic><topic>Labeling</topic><topic>Linear discriminant analysis</topic><topic>Pattern recognition</topic><topic>Performance analysis</topic><topic>Semisupervised learning</topic><topic>Sun</topic><toplevel>online_resources</toplevel><creatorcontrib>Huining Qiu</creatorcontrib><creatorcontrib>Jianhuang Lai</creatorcontrib><creatorcontrib>Jian Huang</creatorcontrib><creatorcontrib>Yu Chen</creatorcontrib><collection>IEEE Electronic Library (IEL) Conference Proceedings</collection><collection>IEEE Proceedings Order Plan All Online (POP All Online) 1998-present by volume</collection><collection>IEEE Xplore All Conference Proceedings</collection><collection>IEEE/IET Electronic Library (IEL)</collection><collection>IEEE Proceedings Order Plans (POP All) 1998-Present</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Huining Qiu</au><au>Jianhuang Lai</au><au>Jian Huang</au><au>Yu Chen</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>Semi-supervised discriminant analysis based on UDP regularization</atitle><btitle>2008 19th International Conference on Pattern Recognition</btitle><stitle>ICPR</stitle><date>2008-12</date><risdate>2008</risdate><spage>1</spage><epage>4</epage><pages>1-4</pages><issn>1051-4651</issn><eissn>2831-7475</eissn><isbn>9781424421749</isbn><isbn>1424421748</isbn><eisbn>9781424421756</eisbn><eisbn>1424421756</eisbn><abstract>We propose a semi-supervised learning algorithm for discriminant analysis, which uses the geometric structure of both labeled and unlabeled samples and perform a manifold regularization on LDA. The labeled data points provide labeling information and the unlabeled data points provide extra geometric structure information of the data, then we learn a labeling function which is as smooth as possible on the data manifold. Experiments on several face databases show the effectiveness of the algorithm.</abstract><pub>IEEE</pub><doi>10.1109/ICPR.2008.4761802</doi><tpages>4</tpages></addata></record> |
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| identifier | ISSN: 1051-4651 |
| ispartof | 2008 19th International Conference on Pattern Recognition, 2008, p.1-4 |
| issn | 1051-4651 2831-7475 |
| language | eng |
| recordid | cdi_ieee_primary_4761802 |
| source | IEEE Electronic Library (IEL) Conference Proceedings |
| subjects | Algorithm design and analysis Face Feature extraction Image databases Labeling Linear discriminant analysis Pattern recognition Performance analysis Semisupervised learning Sun |
| title | Semi-supervised discriminant analysis based on UDP regularization |
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