Dimensionality Reduction by Semantic Mapping in Text Categorization
Identifieur interne : 001819 ( Istex/Corpus ); précédent : 001818; suivant : 001820Dimensionality Reduction by Semantic Mapping in Text Categorization
Auteurs : Renato Fernandes Corrêa ; Teresa Bernarda LudermirSource :
- Lecture Notes in Computer Science [ 0302-9743 ] ; 2004.
English descriptors
- Teeft :
- Algorithm, Categorization, Classification error, Data vectors, Dimensionality, Dimensionality reduction, Document maps, Document vectors, Feature extraction method, Future works, Good alternative, Information retrieval, International conference, Mapping, Mapping method, Matrix, Model vectors, Mutual similarities, Neural networks, Node, Original feature, Original features, Original space, Projection matrix, Random mapping, Renato fernandes, Semantic, Semantic mapping, Semantic maps, Standard deviation, Teresa bernarda ludermir, Text categorization, Text categorization tasks, Websom project.
Abstract
Abstract: In text categorization tasks, the dimensionality reduction become necessary to computation and interpretability of the results generated by machine learning algorithms due to the high-dimensional vector representation of the documents. This paper describes a new feature extraction method called semantic mapping and its application in categorization of web documents. The semantic mapping uses SOM maps to construct variables in reduced space, where each variable describes the behavior of a group of features semantically related. The performance of the semantic mapping is measured and compared empirically with the performance of sparse random mapping and PCA methods and shows to be better than random mapping and a good alternative to PCA.
Url:
DOI: 10.1007/978-3-540-30499-9_160
Links to Exploration step
ISTEX:E8E860D711CA9968CA67FE9C3B27F5CCEB71FC0CLe document en format XML
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This paper describes a new feature extraction method called semantic mapping and its application in categorization of web documents. The semantic mapping uses SOM maps to construct variables in reduced space, where each variable describes the behavior of a group of features semantically related. 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Box 7851, 50.732-970, Recife, PE, Brazil</affiliation></author><author xml:id="author-0001"><persName><forename type="first">Teresa</forename><surname>Ludermir</surname></persName><email>tbl@cin.ufpe.br</email><affiliation>Center of Informatics – Federal University of Pernambuco, Cidade Universitária, P.O. Box 7851, 50.732-970, Recife, PE, Brazil</affiliation></author><idno type="istex">E8E860D711CA9968CA67FE9C3B27F5CCEB71FC0C</idno><idno type="DOI">10.1007/978-3-540-30499-9_160</idno><idno type="ChapterID">160</idno><idno type="ChapterID">Chap160</idno></analytic><monogr><title level="m">Neural Information Processing</title><title level="m" type="sub">11th International Conference, ICONIP 2004, Calcutta, India, November 22-25, 2004. 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University</OrgName><OrgAddress><Country>UK</Country></OrgAddress></Affiliation><Affiliation ID="Aff2"><OrgName>Carnegie Mellon University</OrgName><OrgAddress><City>Pittsburgh</City><State>PA</State><Country>USA</Country></OrgAddress></Affiliation><Affiliation ID="Aff3"><OrgName>University of Surrey</OrgName><OrgAddress><City>Guildford</City><Country>UK</Country></OrgAddress></Affiliation><Affiliation ID="Aff4"><OrgName>Cornell University</OrgName><OrgAddress><City>Ithaca</City><State>NY</State><Country>USA</Country></OrgAddress></Affiliation><Affiliation ID="Aff5"><OrgName>ETH Zurich</OrgName><OrgAddress><Country>Switzerland</Country></OrgAddress></Affiliation><Affiliation ID="Aff6"><OrgName>Stanford University</OrgName><OrgAddress><City>CA</City><Country>USA</Country></OrgAddress></Affiliation><Affiliation ID="Aff7"><OrgName>Weizmann Institute of Science</OrgName><OrgAddress><City>Rehovot</City><Country>Israel</Country></OrgAddress></Affiliation><Affiliation ID="Aff8"><OrgName>University of Bern</OrgName><OrgAddress><Country>Switzerland</Country></OrgAddress></Affiliation><Affiliation ID="Aff9"><OrgName>Indian Institute of Technology</OrgName><OrgAddress><City>Madras</City><Country>India</Country></OrgAddress></Affiliation><Affiliation ID="Aff10"><OrgName>University of Dortmund</OrgName><OrgAddress><Country>Germany</Country></OrgAddress></Affiliation><Affiliation ID="Aff11"><OrgName>Massachusetts Institute of Technology</OrgName><OrgAddress><City>MA</City><Country>USA</Country></OrgAddress></Affiliation><Affiliation ID="Aff12"><OrgName>New York University</OrgName><OrgAddress><City>NY</City><Country>USA</Country></OrgAddress></Affiliation><Affiliation ID="Aff13"><OrgName>University of California</OrgName><OrgAddress><City>Berkeley</City><State>CA</State><Country>USA</Country></OrgAddress></Affiliation><Affiliation ID="Aff14"><OrgName>Rice University</OrgName><OrgAddress><City>Houston</City><State>TX</State><Country>USA</Country></OrgAddress></Affiliation><Affiliation ID="Aff15"><OrgName>Max-Planck Institute of Computer Science</OrgName><OrgAddress><City>Saarbruecken</City><Country>Germany</Country></OrgAddress></Affiliation></EditorGroup></SeriesHeader><Book Language="En"><BookInfo BookProductType="Proceedings" ContainsESM="No" Language="En" MediaType="eBook" NumberingDepth="2" NumberingStyle="ContentOnly" OutputMedium="All" TocLevels="0"><BookID>978-3-540-30499-9</BookID><BookTitle>Neural Information Processing</BookTitle><BookSubTitle>11th International Conference, ICONIP 2004, Calcutta, India, November 22-25, 2004. Proceedings</BookSubTitle><BookVolumeNumber>3316</BookVolumeNumber><BookSequenceNumber>3316</BookSequenceNumber><BookDOI>10.1007/b103766</BookDOI><BookTitleID>114923</BookTitleID><BookPrintISBN>978-3-540-23931-4</BookPrintISBN><BookElectronicISBN>978-3-540-30499-9</BookElectronicISBN><BookChapterCount>210</BookChapterCount><BookCopyright><CopyrightHolderName>Springer-Verlag Berlin Heidelberg</CopyrightHolderName><CopyrightYear>2004</CopyrightYear></BookCopyright><BookSubjectGroup><BookSubject Code="I" Type="Primary">Computer Science</BookSubject><BookSubject Code="I16013" Priority="1" Type="Secondary">Computation by Abstract Devices</BookSubject><BookSubject Code="I21017" Priority="2" Type="Secondary">Artificial Intelligence (incl. Robotics)</BookSubject><BookSubject Code="I2203X" Priority="3" Type="Secondary">Pattern Recognition</BookSubject><BookSubject Code="I22021" Priority="4" Type="Secondary">Image Processing and Computer Vision</BookSubject><BookSubject Code="I17036" Priority="5" Type="Secondary">Probability and Statistics in Computer Science</BookSubject><BookSubject Code="I16021" Priority="6" Type="Secondary">Algorithm Analysis and Problem Complexity</BookSubject><SubjectCollection Code="SUCO11645">Computer Science</SubjectCollection></BookSubjectGroup><BookContext><SeriesID>558</SeriesID></BookContext></BookInfo><BookHeader><EditorGroup><Editor AffiliationIDS="Aff16"><EditorName DisplayOrder="Western"><GivenName>Nikhil</GivenName><GivenName>Ranjan</GivenName><FamilyName>Pal</FamilyName></EditorName><Contact><Email>nikhil@isical.ac.in</Email></Contact></Editor><Editor AffiliationIDS="Aff17"><EditorName DisplayOrder="Western"><GivenName>Nik</GivenName><FamilyName>Kasabov</FamilyName></EditorName><Contact><Email>nkasabov@aut.ac.nz</Email></Contact></Editor><Editor AffiliationIDS="Aff18"><EditorName DisplayOrder="Western"><GivenName>Rajani</GivenName><GivenName>K.</GivenName><FamilyName>Mudi</FamilyName></EditorName><Contact><Email>rkmudi@iee.jusl.ac.in</Email></Contact></Editor><Editor AffiliationIDS="Aff19"><EditorName DisplayOrder="Western"><GivenName>Srimanta</GivenName><FamilyName>Pal</FamilyName></EditorName><Contact><Email>srimanta@isical.ac.in</Email></Contact></Editor><Editor AffiliationIDS="Aff20"><EditorName DisplayOrder="Western"><GivenName>Swapan</GivenName><GivenName>Kumar</GivenName><FamilyName>Parui</FamilyName></EditorName><Contact><Email>swapan@isical.ac.in</Email></Contact></Editor><Affiliation ID="Aff16"><OrgDivision>Indian Statistical Institute</OrgDivision><OrgName>Electronics and Communication Sciences Unit</OrgName><OrgAddress><City>Kolkata</City><Country>India</Country></OrgAddress></Affiliation><Affiliation ID="Aff17"><OrgDivision>School of Computer and Information Sciences, Knowledge Engineering and Discovery Research Institute (KEDRI)</OrgDivision><OrgName>Auckland University of Technology</OrgName><OrgAddress><Postcode>Private Bag 92006</Postcode><City>Auckland</City><Country>New Zealand</Country></OrgAddress></Affiliation><Affiliation ID="Aff18"><OrgDivision>Department of Instrumentation and Electronics Engineering</OrgDivision><OrgName>Jadavpur University</OrgName><OrgAddress><Street>Salt-lake Campus</Street><Postcode>700098</Postcode><City>Calcutta</City><Country>India</Country></OrgAddress></Affiliation><Affiliation ID="Aff19"><OrgName>Indian Statistical Institute</OrgName><OrgAddress><Street>203 B. T. Road</Street><Postcode>700 108</Postcode><City>Calcutta</City><Country> </Country></OrgAddress></Affiliation><Affiliation ID="Aff20"><OrgDivision>Indian Statistical Institute</OrgDivision><OrgName>Computer Vision and Pattern Recognition Unit</OrgName><OrgAddress><Postcode>700108</Postcode><City>Kolkata</City><Country>India</Country></OrgAddress></Affiliation></EditorGroup></BookHeader><Part ID="Part18"><PartInfo TocLevels="0"><PartID>18</PartID><PartSequenceNumber>18</PartSequenceNumber><PartTitle>Feature Analysis</PartTitle><PartChapterCount>5</PartChapterCount><PartContext><SeriesID>558</SeriesID><BookTitle>Neural Information Processing</BookTitle></PartContext></PartInfo><Chapter ID="Chap160" Language="En"><ChapterInfo ChapterType="OriginalPaper" ContainsESM="No" NumberingDepth="2" NumberingStyle="ContentOnly" TocLevels="0"><ChapterID>160</ChapterID><ChapterDOI>10.1007/978-3-540-30499-9_160</ChapterDOI><ChapterSequenceNumber>160</ChapterSequenceNumber><ChapterTitle Language="En">Dimensionality Reduction by Semantic Mapping in Text Categorization</ChapterTitle><ChapterFirstPage>1032</ChapterFirstPage><ChapterLastPage>1037</ChapterLastPage><ChapterCopyright><CopyrightHolderName>Springer-Verlag Berlin Heidelberg</CopyrightHolderName><CopyrightYear>2004</CopyrightYear></ChapterCopyright><ChapterGrants Type="Regular"><MetadataGrant Grant="OpenAccess"></MetadataGrant><AbstractGrant Grant="OpenAccess"></AbstractGrant><BodyPDFGrant Grant="Restricted"></BodyPDFGrant><BodyHTMLGrant Grant="Restricted"></BodyHTMLGrant><BibliographyGrant Grant="Restricted"></BibliographyGrant><ESMGrant Grant="Restricted"></ESMGrant></ChapterGrants><ChapterContext><SeriesID>558</SeriesID><PartID>18</PartID><BookID>978-3-540-30499-9</BookID><BookTitle>Neural Information Processing</BookTitle></ChapterContext></ChapterInfo><ChapterHeader><AuthorGroup><Author AffiliationIDS="Aff21 Aff22"><AuthorName DisplayOrder="Western"><GivenName>Renato</GivenName><GivenName>Fernandes</GivenName><FamilyName>Corrêa</FamilyName></AuthorName><Contact><Email>rfc@cin.ufpe.br</Email></Contact></Author><Author AffiliationIDS="Aff22"><AuthorName DisplayOrder="Western"><GivenName>Teresa</GivenName><GivenName>Bernarda</GivenName><FamilyName>Ludermir</FamilyName></AuthorName><Contact><Email>tbl@cin.ufpe.br</Email></Contact></Author><Affiliation ID="Aff21"><OrgDivision>Polytechnic School</OrgDivision><OrgName>Pernambuco University</OrgName><OrgAddress><Street>Rua Benfica, 455</Street><City>Madalena, Recife</City><State>PE</State><Postcode>50.750-410</Postcode><Country>Brazil</Country></OrgAddress></Affiliation><Affiliation ID="Aff22"><OrgName>Center of Informatics – Federal University of Pernambuco</OrgName><OrgAddress><Postbox>P.O. Box 7851</Postbox><Street>Cidade Universitária</Street><City>Recife</City><State>PE</State><Postcode>50.732-970</Postcode><Country>Brazil</Country></OrgAddress></Affiliation></AuthorGroup><Abstract ID="Abs1" Language="En"><Heading>Abstract</Heading><Para>In text categorization tasks, the dimensionality reduction become necessary to computation and interpretability of the results generated by machine learning algorithms due to the high-dimensional vector representation of the documents. This paper describes a new feature extraction method called semantic mapping and its application in categorization of web documents. The semantic mapping uses SOM maps to construct variables in reduced space, where each variable describes the behavior of a group of features semantically related. The performance of the semantic mapping is measured and compared empirically with the performance of sparse random mapping and PCA methods and shows to be better than random mapping and a good alternative to PCA.</Para></Abstract></ChapterHeader><NoBody></NoBody></Chapter></Part></Book></Series></Publisher></istex:document></istex:metadataXml><mods version="3.6"><titleInfo lang="en"><title>Dimensionality Reduction by Semantic Mapping in Text Categorization</title></titleInfo><titleInfo type="alternative" contentType="CDATA" lang="en"><title>Dimensionality Reduction by Semantic Mapping in Text Categorization</title></titleInfo><name type="personal"><namePart type="given">Renato</namePart><namePart type="given">Fernandes</namePart><namePart type="family">Corrêa</namePart><affiliation>Polytechnic School, Pernambuco University, Rua Benfica, 455, 50.750-410, Madalena, Recife, PE, Brazil</affiliation><affiliation>Center of Informatics – Federal University of Pernambuco, Cidade Universitária, P.O. Box 7851, 50.732-970, Recife, PE, Brazil</affiliation><affiliation>E-mail: rfc@cin.ufpe.br</affiliation><role><roleTerm type="text">author</roleTerm></role></name><name type="personal"><namePart type="given">Teresa</namePart><namePart type="given">Bernarda</namePart><namePart type="family">Ludermir</namePart><affiliation>Center of Informatics – Federal University of Pernambuco, Cidade Universitária, P.O. Box 7851, 50.732-970, Recife, PE, Brazil</affiliation><affiliation>E-mail: tbl@cin.ufpe.br</affiliation><role><roleTerm type="text">author</roleTerm></role></name><typeOfResource>text</typeOfResource><genre type="conference" displayLabel="OriginalPaper"></genre><originInfo><publisher>Springer Berlin Heidelberg</publisher><place><placeTerm type="text">Berlin, Heidelberg</placeTerm></place><dateIssued encoding="w3cdtf">2004</dateIssued><copyrightDate encoding="w3cdtf">2004</copyrightDate></originInfo><language><languageTerm type="code" authority="rfc3066">en</languageTerm><languageTerm type="code" authority="iso639-2b">eng</languageTerm></language><physicalDescription><internetMediaType>text/html</internetMediaType></physicalDescription><abstract lang="en">Abstract: In text categorization tasks, the dimensionality reduction become necessary to computation and interpretability of the results generated by machine learning algorithms due to the high-dimensional vector representation of the documents. This paper describes a new feature extraction method called semantic mapping and its application in categorization of web documents. The semantic mapping uses SOM maps to construct variables in reduced space, where each variable describes the behavior of a group of features semantically related. The performance of the semantic mapping is measured and compared empirically with the performance of sparse random mapping and PCA methods and shows to be better than random mapping and a good alternative to PCA.</abstract><relatedItem type="host"><titleInfo><title>Neural Information Processing</title><subTitle>11th International Conference, ICONIP 2004, Calcutta, India, November 22-25, 2004. Proceedings</subTitle></titleInfo><name type="personal"><namePart type="given">Nikhil</namePart><namePart type="given">Ranjan</namePart><namePart type="family">Pal</namePart><affiliation>Indian Statistical Institute, Electronics and Communication Sciences Unit, Kolkata, India</affiliation><affiliation>E-mail: nikhil@isical.ac.in</affiliation><role><roleTerm type="text">editor</roleTerm></role></name><name type="personal"><namePart type="given">Nik</namePart><namePart type="family">Kasabov</namePart><affiliation>School of Computer and Information Sciences, Knowledge Engineering and Discovery Research Institute (KEDRI), Auckland University of Technology, Private Bag 92006, Auckland, New Zealand</affiliation><affiliation>E-mail: nkasabov@aut.ac.nz</affiliation><role><roleTerm type="text">editor</roleTerm></role></name><name type="personal"><namePart type="given">Rajani</namePart><namePart type="given">K.</namePart><namePart type="family">Mudi</namePart><affiliation>Department of Instrumentation and Electronics Engineering, Jadavpur University, Salt-lake Campus, 700098, Calcutta, India</affiliation><affiliation>E-mail: rkmudi@iee.jusl.ac.in</affiliation><role><roleTerm type="text">editor</roleTerm></role></name><name type="personal"><namePart type="given">Srimanta</namePart><namePart type="family">Pal</namePart><affiliation>Indian Statistical Institute, 203 B. T. Road, 700 108, Calcutta</affiliation><affiliation>E-mail: srimanta@isical.ac.in</affiliation><role><roleTerm type="text">editor</roleTerm></role></name><name type="personal"><namePart type="given">Swapan</namePart><namePart type="given">Kumar</namePart><namePart type="family">Parui</namePart><affiliation>Indian Statistical Institute, Computer Vision and Pattern Recognition Unit, 700108, Kolkata, India</affiliation><affiliation>E-mail: swapan@isical.ac.in</affiliation><role><roleTerm type="text">editor</roleTerm></role></name><genre type="book-series" displayLabel="Proceedings"></genre><originInfo><copyrightDate encoding="w3cdtf">2004</copyrightDate><issuance>monographic</issuance></originInfo><subject><genre>Book-Subject-Collection</genre><topic authority="SpringerSubjectCodes" authorityURI="SUCO11645">Computer Science</topic></subject><subject><genre>Book-Subject-Group</genre><topic authority="SpringerSubjectCodes" authorityURI="I">Computer Science</topic><topic authority="SpringerSubjectCodes" authorityURI="I16013">Computation by Abstract Devices</topic><topic authority="SpringerSubjectCodes" authorityURI="I21017">Artificial Intelligence (incl. Robotics)</topic><topic authority="SpringerSubjectCodes" authorityURI="I2203X">Pattern Recognition</topic><topic authority="SpringerSubjectCodes" authorityURI="I22021">Image Processing and Computer Vision</topic><topic authority="SpringerSubjectCodes" authorityURI="I17036">Probability and Statistics in Computer Science</topic><topic authority="SpringerSubjectCodes" authorityURI="I16021">Algorithm Analysis and Problem Complexity</topic></subject><identifier type="DOI">10.1007/b103766</identifier><identifier type="ISBN">978-3-540-23931-4</identifier><identifier type="eISBN">978-3-540-30499-9</identifier><identifier type="ISSN">0302-9743</identifier><identifier type="eISSN">1611-3349</identifier><identifier type="BookTitleID">114923</identifier><identifier type="BookID">978-3-540-30499-9</identifier><identifier type="BookChapterCount">210</identifier><identifier type="BookVolumeNumber">3316</identifier><identifier type="BookSequenceNumber">3316</identifier><identifier type="PartChapterCount">5</identifier><part><date>2004</date><detail type="part"><title>Feature Analysis</title></detail><detail type="volume"><number>3316</number><caption>vol.</caption></detail><extent unit="pages"><start>1032</start><end>1037</end></extent></part><recordInfo><recordOrigin>Springer-Verlag Berlin Heidelberg, 2004</recordOrigin></recordInfo></relatedItem><relatedItem type="series"><titleInfo><title>Lecture Notes in Computer Science</title></titleInfo><name type="personal"><namePart type="given">David</namePart><namePart type="family">Hutchison</namePart><affiliation>Lancaster University, UK</affiliation><role><roleTerm type="text">editor</roleTerm></role></name><name type="personal"><namePart type="given">Takeo</namePart><namePart type="family">Kanade</namePart><affiliation>Carnegie Mellon University, Pittsburgh, PA, USA</affiliation><role><roleTerm type="text">editor</roleTerm></role></name><name type="personal"><namePart type="given">Josef</namePart><namePart type="family">Kittler</namePart><affiliation>University of Surrey, Guildford, UK</affiliation><role><roleTerm type="text">editor</roleTerm></role></name><name type="personal"><namePart type="given">Jon</namePart><namePart type="given">M.</namePart><namePart type="family">Kleinberg</namePart><affiliation>Cornell University, Ithaca, NY, USA</affiliation><role><roleTerm type="text">editor</roleTerm></role></name><name type="personal"><namePart type="given">Friedemann</namePart><namePart type="family">Mattern</namePart><affiliation>ETH Zurich, Switzerland</affiliation><role><roleTerm type="text">editor</roleTerm></role></name><name type="personal"><namePart type="given">John</namePart><namePart type="given">C.</namePart><namePart type="family">Mitchell</namePart><affiliation>Stanford University, CA, USA</affiliation><role><roleTerm type="text">editor</roleTerm></role></name><name type="personal"><namePart type="given">Moni</namePart><namePart 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type="text">editor</roleTerm></role></name><originInfo><copyrightDate encoding="w3cdtf">2004</copyrightDate><issuance>serial</issuance></originInfo><identifier type="ISSN">0302-9743</identifier><identifier type="eISSN">1611-3349</identifier><identifier type="SeriesID">558</identifier><recordInfo><recordOrigin>Springer-Verlag Berlin Heidelberg, 2004</recordOrigin></recordInfo></relatedItem><identifier type="istex">E8E860D711CA9968CA67FE9C3B27F5CCEB71FC0C</identifier><identifier type="DOI">10.1007/978-3-540-30499-9_160</identifier><identifier type="ChapterID">160</identifier><identifier type="ChapterID">Chap160</identifier><accessCondition type="use and reproduction" contentType="copyright">Springer-Verlag Berlin Heidelberg, 2004</accessCondition><recordInfo><recordContentSource>SPRINGER</recordContentSource><recordOrigin>Springer-Verlag Berlin Heidelberg, 2004</recordOrigin></recordInfo></mods></metadata></istex></record>Pour manipuler ce document sous Unix (Dilib)
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