Nonnegative singular value decomposition for microarray data analysis of spermatogenesis
Matrix factorization plays an important role in scientific computation. The widely used one is singular value decomposition (SVD) which approximates the original data matrix with three lower rank matrices with orthogonality constraints. Recently nonnegative matrix factorization (NMF) considering the...
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| creator | Weixiang Liu Aifa Tang Datian Ye Zhen Ji |
| description | Matrix factorization plays an important role in scientific computation. The widely used one is singular value decomposition (SVD) which approximates the original data matrix with three lower rank matrices with orthogonality constraints. Recently nonnegative matrix factorization (NMF) considering the nonnegativity of data makes the results more interpretable than those of SVD. However NMF finds only two factor matrices and there is no significant index as singular values of SVD which can be used for sorting learned basis vectors. In this paper we take into account the nonnegativity for SVD and propose nonnegative SVD (NNSVD). The preliminary results on the microarray data of spermatogenesis show that NNSVD has advantages of both SVD and NMF. |
| doi_str_mv | 10.1109/ITAB.2008.4570528 |
| format | Conference Proceeding |
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The widely used one is singular value decomposition (SVD) which approximates the original data matrix with three lower rank matrices with orthogonality constraints. Recently nonnegative matrix factorization (NMF) considering the nonnegativity of data makes the results more interpretable than those of SVD. However NMF finds only two factor matrices and there is no significant index as singular values of SVD which can be used for sorting learned basis vectors. In this paper we take into account the nonnegativity for SVD and propose nonnegative SVD (NNSVD). 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The widely used one is singular value decomposition (SVD) which approximates the original data matrix with three lower rank matrices with orthogonality constraints. Recently nonnegative matrix factorization (NMF) considering the nonnegativity of data makes the results more interpretable than those of SVD. However NMF finds only two factor matrices and there is no significant index as singular values of SVD which can be used for sorting learned basis vectors. In this paper we take into account the nonnegativity for SVD and propose nonnegative SVD (NNSVD). The preliminary results on the microarray data of spermatogenesis show that NNSVD has advantages of both SVD and NMF.</description><subject>Biomedical computing</subject><subject>Biomedical engineering</subject><subject>Data analysis</subject><subject>Data engineering</subject><subject>Gene expression</subject><subject>Information technology</subject><subject>Matrix decomposition</subject><subject>Pattern analysis</subject><subject>Singular value decomposition</subject><subject>Sorting</subject><issn>2168-2194</issn><issn>2168-2208</issn><isbn>9781424422548</isbn><isbn>142442254X</isbn><isbn>1424422558</isbn><isbn>9781424422555</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2008</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><sourceid>RIE</sourceid><recordid>eNo1kE1rAjEYhNMPoWr9AaWX_IG1STbZJEcr_RCkvXjoTV43byRldyPJKvjvu6V1LgPzwMAMIQ-czTln9mm1WTzPBWNmLpVmSpgrMuFSSCmEUuaajAWvTCEEMzdkZrW5MGluL4xbOSKT3w7LuK3kHZnl_M0GmYHbaky-PmLX4R76cEKaQ7c_NpDoCZojUod1bA8xhz7EjvqYaBvqFCElOFMHPVDooDnnkGn0NB8wtdDHPXY4RPdk5KHJOPv3Kdm8vmyW78X68221XKyLYFlflE6X1glQkgmsrbJguTLeqcqzXeVQSthpsAZFyZyrvYZaMdRmGGm1l76ckse_2oCI20MKLaTz9v-w8gddSFqI</recordid><startdate>200805</startdate><enddate>200805</enddate><creator>Weixiang Liu</creator><creator>Aifa Tang</creator><creator>Datian Ye</creator><creator>Zhen Ji</creator><general>IEEE</general><scope>6IE</scope><scope>6IL</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIL</scope></search><sort><creationdate>200805</creationdate><title>Nonnegative singular value decomposition for microarray data analysis of spermatogenesis</title><author>Weixiang Liu ; Aifa Tang ; Datian Ye ; Zhen Ji</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-i90t-3d739d2a5402ec959a9158fd56f0b6de44ab7a98e230ddcf7ac50e7854897f4f3</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2008</creationdate><topic>Biomedical computing</topic><topic>Biomedical engineering</topic><topic>Data analysis</topic><topic>Data engineering</topic><topic>Gene expression</topic><topic>Information technology</topic><topic>Matrix decomposition</topic><topic>Pattern analysis</topic><topic>Singular value decomposition</topic><topic>Sorting</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Weixiang Liu</creatorcontrib><creatorcontrib>Aifa Tang</creatorcontrib><creatorcontrib>Datian Ye</creatorcontrib><creatorcontrib>Zhen Ji</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 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>Weixiang Liu</au><au>Aifa Tang</au><au>Datian Ye</au><au>Zhen Ji</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>Nonnegative singular value decomposition for microarray data analysis of spermatogenesis</atitle><btitle>2008 International Conference on Information Technology and Applications in Biomedicine</btitle><stitle>ITAB</stitle><date>2008-05</date><risdate>2008</risdate><spage>225</spage><epage>228</epage><pages>225-228</pages><issn>2168-2194</issn><eissn>2168-2208</eissn><isbn>9781424422548</isbn><isbn>142442254X</isbn><eisbn>1424422558</eisbn><eisbn>9781424422555</eisbn><abstract>Matrix factorization plays an important role in scientific computation. The widely used one is singular value decomposition (SVD) which approximates the original data matrix with three lower rank matrices with orthogonality constraints. Recently nonnegative matrix factorization (NMF) considering the nonnegativity of data makes the results more interpretable than those of SVD. However NMF finds only two factor matrices and there is no significant index as singular values of SVD which can be used for sorting learned basis vectors. In this paper we take into account the nonnegativity for SVD and propose nonnegative SVD (NNSVD). The preliminary results on the microarray data of spermatogenesis show that NNSVD has advantages of both SVD and NMF.</abstract><pub>IEEE</pub><doi>10.1109/ITAB.2008.4570528</doi><tpages>4</tpages></addata></record> |
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| language | eng |
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| source | IEEE Electronic Library (IEL) Conference Proceedings |
| subjects | Biomedical computing Biomedical engineering Data analysis Data engineering Gene expression Information technology Matrix decomposition Pattern analysis Singular value decomposition Sorting |
| title | Nonnegative singular value decomposition for microarray data analysis of spermatogenesis |
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