Information filtering using the Riemannian SVD (R-SVD)
The Riemannian SVD (or R-SVD) is a recent nonlinear generalization of the SVD which has been used for specific applications in systems and control. This decomposition can be modified and used to formulate a filtering-based implementation of Latent Semantic Indexing (LSI) for conceptual information r...
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| creator | Jiang, Eric P. Berry, Michael W. |
| description | The Riemannian SVD (or R-SVD) is a recent nonlinear generalization of the SVD which has been used for specific applications in systems and control. This decomposition can be modified and used to formulate a filtering-based implementation of Latent Semantic Indexing (LSI) for conceptual information retrieval. With LSI, the underlying semantic structure of a collection is represented in k-dimensional space using a rank-k approximation to the corresponding (sparse) term-bydocument matrix. Updating LSI models based on user feedback can be accomplished using constraints modeled by the R-SVD of a low-rank approximation to the original term-by-document matrix. |
| doi_str_mv | 10.1007/BFb0018555 |
| format | Conference Proceeding |
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Documentation</subject><subject>Latent Semantic Indexing</subject><subject>Relevance Feedback</subject><subject>Sciences and techniques of general use</subject><subject>Simulation</subject><subject>Singular Value Decomposition</subject><subject>Singular Vector</subject><subject>Software</subject><subject>System design and modelling</subject><subject>Total Little Square</subject><issn>0302-9743</issn><issn>1611-3349</issn><isbn>3540648097</isbn><isbn>9783540648093</isbn><isbn>3540685332</isbn><isbn>9783540685333</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2005</creationdate><recordtype>conference_proceeding</recordtype><recordid>eNpFkEtPwzAQhM1LopRe-AU5cCiHwNprx_YRCoVKlZDK4xrZjgOG1onicuDfk6iVmMPOYT6tRkPIBYVrCiBv7uYWgCohxAE5Q8GhUAKRHZIRLSjNEbk-2gdcgZbHZAQILNeS4ymZpPQFvZAVUsgRKRaxbrqN2YYmZnVYb30X4kf2k4a7_fTZKviNiTGYmL2832fTVd7b1Tk5qc06-cnex-Rt_vA6e8qXz4-L2e0ybymCyJE6yTg6qZxVlde20EJ40EKB01rTitUefO0dtaLHmDXSI0OUAi1nvMIxudz9bU1yZl13JrqQyrYLG9P9lowpRSnvsekOS-1Q33elbZrvVFIoh83K_83wD5vsVss</recordid><startdate>20050609</startdate><enddate>20050609</enddate><creator>Jiang, Eric P.</creator><creator>Berry, Michael W.</creator><general>Springer Berlin Heidelberg</general><general>Springer</general><scope>IQODW</scope></search><sort><creationdate>20050609</creationdate><title>Information filtering using the Riemannian SVD (R-SVD)</title><author>Jiang, Eric P. ; Berry, Michael W.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-p1305-31c7243c78cb8de9b6955e09580c9991d2fe0efec1b543c2ba7e3233753b424d3</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2005</creationdate><topic>Applied sciences</topic><topic>Computer science; control theory; systems</topic><topic>Exact sciences and technology</topic><topic>Information and communication sciences</topic><topic>Information retrieval systems. Information and document management system</topic><topic>Information science. Documentation</topic><topic>Latent Semantic Indexing</topic><topic>Relevance Feedback</topic><topic>Sciences and techniques of general use</topic><topic>Simulation</topic><topic>Singular Value Decomposition</topic><topic>Singular Vector</topic><topic>Software</topic><topic>System design and modelling</topic><topic>Total Little Square</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Jiang, Eric P.</creatorcontrib><creatorcontrib>Berry, Michael W.</creatorcontrib><collection>Pascal-Francis</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Jiang, Eric P.</au><au>Berry, Michael W.</au><au>Rolim, José</au><au>Simon, Horst</au><au>Ferreira, Alfonso</au><au>Teng, Shang-Hua</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>Information filtering using the Riemannian SVD (R-SVD)</atitle><btitle>Lecture notes in computer science</btitle><date>2005-06-09</date><risdate>2005</risdate><volume>1457</volume><spage>386</spage><epage>395</epage><pages>386-395</pages><issn>0302-9743</issn><eissn>1611-3349</eissn><isbn>3540648097</isbn><isbn>9783540648093</isbn><eisbn>3540685332</eisbn><eisbn>9783540685333</eisbn><abstract>The Riemannian SVD (or R-SVD) is a recent nonlinear generalization of the SVD which has been used for specific applications in systems and control. 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| fulltext | fulltext |
| identifier | ISSN: 0302-9743 |
| ispartof | Lecture notes in computer science, 2005, Vol.1457, p.386-395 |
| issn | 0302-9743 1611-3349 |
| language | eng |
| recordid | cdi_pascalfrancis_primary_2288114 |
| source | Springer Books |
| subjects | Applied sciences Computer science control theory systems Exact sciences and technology Information and communication sciences Information retrieval systems. Information and document management system Information science. Documentation Latent Semantic Indexing Relevance Feedback Sciences and techniques of general use Simulation Singular Value Decomposition Singular Vector Software System design and modelling Total Little Square |
| title | Information filtering using the Riemannian SVD (R-SVD) |
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