Learning of Variability for Invariant Statistical Pattern Recognition
Identifieur interne : 001149 ( Istex/Corpus ); précédent : 001148; suivant : 001150Learning of Variability for Invariant Statistical Pattern Recognition
Auteurs : Daniel Keysers ; Wolfgang Macherey ; Jörg Dahmen ; Hermann NeySource :
- Lecture Notes in Computer Science [ 0302-9743 ] ; 2001.
Abstract
Abstract: In many applications, modelling techniques are necessary which take into account the inherent variability of given data. In this paper, we present an approach to model class specific pattern variation based on tangent distance within a statistical framework for classification. The model is an effective means to explicitly incorporate invariance with respect to transformations that do not change class-membership like e.g. small affine transformations in the case of image objects. If no prior knowledge about the type of variability is available, it is desirable to learn the model parameters from the data. The probabilistic interpretation presented here allows us to view learning of the variational derivatives in terms of a maximum likelihood estimation problem. We present experimental results from two different real-world pattern recognition tasks, namely image object recognition and automatic speech recognition. On the US Postal Service handwritten digit recognition task, learning of variability achieves results well comparable to those obtained using specific domain knowledge. On the SieTill corpus for continuously spoken telephone line recorded German digit strings the method shows a significant improvement in comparison with a common mixture density approach using a comparable amount of parameters. The probabilistic model is well-suited to be used in the field of statistical pattern recognition and can be extended to other domains like cluster analysis.
Url:
DOI: 10.1007/3-540-44795-4_23
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In this paper, we present an approach to model class specific pattern variation based on tangent distance within a statistical framework for classification. The model is an effective means to explicitly incorporate invariance with respect to transformations that do not change class-membership like e.g. small affine transformations in the case of image objects. If no prior knowledge about the type of variability is available, it is desirable to learn the model parameters from the data. The probabilistic interpretation presented here allows us to view learning of the variational derivatives in terms of a maximum likelihood estimation problem. We present experimental results from two different real-world pattern recognition tasks, namely image object recognition and automatic speech recognition. On the US Postal Service handwritten digit recognition task, learning of variability achieves results well comparable to those obtained using specific domain knowledge. On the SieTill corpus for continuously spoken telephone line recorded German digit strings the method shows a significant improvement in comparison with a common mixture density approach using a comparable amount of parameters. The probabilistic model is well-suited to be used in the field of statistical pattern recognition and can be extended to other domains like cluster analysis.</div></front></TEI><istex><corpusName>springer</corpusName><author><json:item><name>Daniel Keysers</name><affiliations><json:string>Lehrstuhl für Informatik VI, Computer Science Department, RWTH Aachen - University of Technology, D-52056, Aachen, Germany</json:string><json:string>E-mail: keysers@informatik.rwth-aachen.de</json:string></affiliations></json:item><json:item><name>Wolfgang Macherey</name><affiliations><json:string>Lehrstuhl für Informatik VI, Computer Science Department, RWTH Aachen - University of Technology, D-52056, Aachen, Germany</json:string><json:string>E-mail: w.macherey@informatik.rwth-aachen.de</json:string></affiliations></json:item><json:item><name>Jörg Dahmen</name><affiliations><json:string>Lehrstuhl für Informatik VI, Computer Science Department, RWTH Aachen - University of Technology, D-52056, Aachen, Germany</json:string><json:string>E-mail: dahmen@informatik.rwth-aachen.de</json:string></affiliations></json:item><json:item><name>Hermann Ney</name><affiliations><json:string>Lehrstuhl für Informatik VI, Computer Science Department, RWTH Aachen - University of Technology, D-52056, Aachen, Germany</json:string><json:string>E-mail: ney@informatik.rwth-aachen.de</json:string></affiliations></json:item></author><language><json:string>eng</json:string></language><abstract>Abstract: In many applications, modelling techniques are necessary which take into account the inherent variability of given data. 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On the SieTill corpus for continuously spoken telephone line recorded German digit strings the method shows a significant improvement in comparison with a common mixture density approach using a comparable amount of parameters. 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In this paper, we present an approach to model class specific pattern variation based on tangent distance within a statistical framework for classification. The model is an effective means to explicitly incorporate invariance with respect to transformations that do not change class-membership like e.g. small affine transformations in the case of image objects. If no prior knowledge about the type of variability is available, it is desirable to learn the model parameters from the data. The probabilistic interpretation presented here allows us to view learning of the variational derivatives in terms of a maximum likelihood estimation problem. We present experimental results from two different real-world pattern recognition tasks, namely image object recognition and automatic speech recognition. On the US Postal Service handwritten digit recognition task, learning of variability achieves results well comparable to those obtained using specific domain knowledge. On the SieTill corpus for continuously spoken telephone line recorded German digit strings the method shows a significant improvement in comparison with a common mixture density approach using a comparable amount of parameters. The probabilistic model is well-suited to be used in the field of statistical pattern recognition and can be extended to other domains like cluster analysis.</p></abstract><textClass><keywords scheme="Book Subject Collection"><list><label>SUCO11645</label><item><term>Computer Science</term></item></list></keywords></textClass><textClass><keywords scheme="Book Subject Group"><list><label>I</label><label>I21017</label><label>I16021</label><label>I16048</label><item><term>Computer Science</term></item><item><term>Artificial Intelligence (incl. 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Robotics)</BookSubject><BookSubject Code="I16021" Priority="2" Type="Secondary">Algorithm Analysis and Problem Complexity</BookSubject><BookSubject Code="I16048" Priority="3" Type="Secondary">Mathematical Logic and Formal Languages</BookSubject><SubjectCollection Code="SUCO11645">Computer Science</SubjectCollection></BookSubjectGroup><BookContext><SeriesID>558</SeriesID></BookContext></BookInfo><BookHeader><EditorGroup><Editor AffiliationIDS="Aff1"><EditorName DisplayOrder="Western"><GivenName>Luc</GivenName><Particle>De</Particle><FamilyName>Raedt</FamilyName></EditorName><Contact><Email>deraedt@informatik.uni-freiburg.de</Email></Contact></Editor><Editor AffiliationIDS="Aff2"><EditorName DisplayOrder="Western"><GivenName>Peter</GivenName><FamilyName>Flach</FamilyName></EditorName><Contact><Email>peter.flach@bristol.ac.uk</Email></Contact></Editor><Affiliation ID="Aff1"><OrgDivision>Department of Computer Science</OrgDivision><OrgName>Albert-Ludwigs University Freiburg</OrgName><OrgAddress><Street>Georges Köhler-Allee, Geb. 079</Street><Postcode>79110</Postcode><City>Freiburg</City><Country>Germany</Country></OrgAddress></Affiliation><Affiliation ID="Aff2"><OrgDivision>Department of Computer Science</OrgDivision><OrgName>University of Bristol</OrgName><OrgAddress><Street>Merchant Ventures Bldg., Woodland Road</Street><City>Bristol</City><Postcode>BS8 1UB</Postcode><Country>UK</Country></OrgAddress></Affiliation></EditorGroup></BookHeader><Part ID="Part1"><PartInfo TocLevels="0"><PartID>1</PartID><PartSequenceNumber>1</PartSequenceNumber><PartTitle>Regular Papers</PartTitle><PartChapterCount>50</PartChapterCount><PartContext><SeriesID>558</SeriesID><BookID>3-540-44795-4</BookID><BookTitle>Machine Learning: ECML 2001</BookTitle></PartContext></PartInfo><Chapter ID="Chap23" Language="En"><ChapterInfo ChapterType="OriginalPaper" ContainsESM="No" Language="En" NumberingStyle="Unnumbered" TocLevels="0"><ChapterID>23</ChapterID><ChapterDOI>10.1007/3-540-44795-4_23</ChapterDOI><ChapterSequenceNumber>23</ChapterSequenceNumber><ChapterTitle Language="En">Learning of Variability for Invariant Statistical Pattern Recognition</ChapterTitle><ChapterFirstPage>263</ChapterFirstPage><ChapterLastPage>275</ChapterLastPage><ChapterCopyright><CopyrightHolderName>Springer-Verlag Berlin Heidelberg</CopyrightHolderName><CopyrightYear>2001</CopyrightYear></ChapterCopyright><ChapterHistory><RegistrationDate><Year>2001</Year><Month>8</Month><Day>29</Day></RegistrationDate><OnlineDate><Year>2001</Year><Month>8</Month><Day>30</Day></OnlineDate></ChapterHistory><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>1</PartID><BookID>3-540-44795-4</BookID><BookTitle>Machine Learning: ECML 2001</BookTitle></ChapterContext></ChapterInfo><ChapterHeader><AuthorGroup><Author AffiliationIDS="Aff5"><AuthorName DisplayOrder="Western"><GivenName>Daniel</GivenName><FamilyName>Keysers</FamilyName></AuthorName><Contact><Email>keysers@informatik.rwth-aachen.de</Email></Contact></Author><Author AffiliationIDS="Aff5"><AuthorName DisplayOrder="Western"><GivenName>Wolfgang</GivenName><FamilyName>Macherey</FamilyName></AuthorName><Contact><Email>w.macherey@informatik.rwth-aachen.de</Email></Contact></Author><Author AffiliationIDS="Aff5"><AuthorName DisplayOrder="Western"><GivenName>Jörg</GivenName><FamilyName>Dahmen</FamilyName></AuthorName><Contact><Email>dahmen@informatik.rwth-aachen.de</Email></Contact></Author><Author AffiliationIDS="Aff5"><AuthorName DisplayOrder="Western"><GivenName>Hermann</GivenName><FamilyName>Ney</FamilyName></AuthorName><Contact><Email>ney@informatik.rwth-aachen.de</Email></Contact></Author><Affiliation ID="Aff5"><OrgDivision>Lehrstuhl für Informatik VI, Computer Science Department</OrgDivision><OrgName>RWTH Aachen - University of Technology</OrgName><OrgAddress><Postcode>D-52056</Postcode><City>Aachen</City><Country>Germany</Country></OrgAddress></Affiliation></AuthorGroup><Abstract ID="Abs1" Language="En"><Heading>Abstract</Heading><Para>In many applications, modelling techniques are necessary which take into account the inherent variability of given data. In this paper, we present an approach to model class specific pattern variation based on tangent distance within a statistical framework for classification. The model is an effective means to explicitly incorporate invariance with respect to transformations that do not change class-membership like e.g. small affine transformations in the case of image objects. If no prior knowledge about the type of variability is available, it is desirable to learn the model parameters from the data. The probabilistic interpretation presented here allows us to view learning of the variational derivatives in terms of a maximum likelihood estimation problem. We present experimental results from two different real-world pattern recognition tasks, namely image object recognition and automatic speech recognition. On the US Postal Service handwritten digit recognition task, learning of variability achieves results well comparable to those obtained using specific domain knowledge. On the SieTill corpus for continuously spoken telephone line recorded German digit strings the method shows a significant improvement in comparison with a common mixture density approach using a comparable amount of parameters. The probabilistic model is well-suited to be used in the field of statistical pattern recognition and can be extended to other domains like cluster analysis.</Para></Abstract></ChapterHeader><NoBody></NoBody></Chapter></Part></Book><SubSeries><SubSeriesInfo><SubSeriesID>1244</SubSeriesID><SubSeriesTitle Language="En">Lecture Notes in Artificial Intelligence</SubSeriesTitle><SubSeriesSubTitle Language="En">Subseries of Lecture Notes in Computer Science</SubSeriesSubTitle></SubSeriesInfo><SubSeriesHeader><EditorGroup><Editor AffiliationIDS="Aff3"><EditorName DisplayOrder="Western"><GivenName>Jaime</GivenName><GivenName>G.</GivenName><FamilyName>Carbonell</FamilyName></EditorName></Editor><Editor AffiliationIDS="Aff4"><EditorName DisplayOrder="Western"><GivenName>Jörg</GivenName><FamilyName>Siekmann</FamilyName></EditorName></Editor><Affiliation ID="Aff3"><OrgName>Carnegie Mellon University</OrgName><OrgAddress><City>Pittsburgh</City><State>PA</State><Country>USA</Country></OrgAddress></Affiliation><Affiliation ID="Aff4"><OrgName>University of Saarland</OrgName><OrgAddress><City>Saarbrücken</City><Country>Germany</Country></OrgAddress></Affiliation></EditorGroup></SubSeriesHeader></SubSeries></Series></Publisher></istex:document></istex:metadataXml><mods version="3.6"><titleInfo lang="en"><title>Learning of Variability for Invariant Statistical Pattern Recognition</title></titleInfo><titleInfo type="alternative" contentType="CDATA" lang="en"><title>Learning of Variability for Invariant Statistical Pattern Recognition</title></titleInfo><name type="personal"><namePart type="given">Daniel</namePart><namePart type="family">Keysers</namePart><affiliation>Lehrstuhl für Informatik VI, Computer Science Department, RWTH Aachen - University of Technology, D-52056, Aachen, Germany</affiliation><affiliation>E-mail: keysers@informatik.rwth-aachen.de</affiliation><role><roleTerm type="text">author</roleTerm></role></name><name type="personal"><namePart type="given">Wolfgang</namePart><namePart type="family">Macherey</namePart><affiliation>Lehrstuhl für Informatik VI, Computer Science Department, RWTH Aachen - University of Technology, D-52056, Aachen, Germany</affiliation><affiliation>E-mail: w.macherey@informatik.rwth-aachen.de</affiliation><role><roleTerm type="text">author</roleTerm></role></name><name type="personal"><namePart type="given">Jörg</namePart><namePart type="family">Dahmen</namePart><affiliation>Lehrstuhl für Informatik VI, Computer Science Department, RWTH Aachen - University of Technology, D-52056, Aachen, Germany</affiliation><affiliation>E-mail: dahmen@informatik.rwth-aachen.de</affiliation><role><roleTerm type="text">author</roleTerm></role></name><name type="personal"><namePart type="given">Hermann</namePart><namePart type="family">Ney</namePart><affiliation>Lehrstuhl für Informatik VI, Computer Science Department, RWTH Aachen - University of Technology, D-52056, Aachen, Germany</affiliation><affiliation>E-mail: ney@informatik.rwth-aachen.de</affiliation><role><roleTerm type="text">author</roleTerm></role></name><typeOfResource>text</typeOfResource><genre type="conference [eBooks]" displayLabel="OriginalPaper"></genre><originInfo><publisher>Springer Berlin Heidelberg</publisher><place><placeTerm type="text">Berlin, Heidelberg</placeTerm></place><dateIssued encoding="w3cdtf">2001</dateIssued><copyrightDate encoding="w3cdtf">2001</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 many applications, modelling techniques are necessary which take into account the inherent variability of given data. In this paper, we present an approach to model class specific pattern variation based on tangent distance within a statistical framework for classification. The model is an effective means to explicitly incorporate invariance with respect to transformations that do not change class-membership like e.g. small affine transformations in the case of image objects. If no prior knowledge about the type of variability is available, it is desirable to learn the model parameters from the data. The probabilistic interpretation presented here allows us to view learning of the variational derivatives in terms of a maximum likelihood estimation problem. We present experimental results from two different real-world pattern recognition tasks, namely image object recognition and automatic speech recognition. On the US Postal Service handwritten digit recognition task, learning of variability achieves results well comparable to those obtained using specific domain knowledge. On the SieTill corpus for continuously spoken telephone line recorded German digit strings the method shows a significant improvement in comparison with a common mixture density approach using a comparable amount of parameters. The probabilistic model is well-suited to be used in the field of statistical pattern recognition and can be extended to other domains like cluster analysis.</abstract><relatedItem type="host"><titleInfo><title>Machine Learning: ECML 2001</title><subTitle>12th European Conference on Machine Learning Freiburg, Germany, September 5–7, 2001 Proceedings</subTitle></titleInfo><name type="personal"><namePart type="given">Luc</namePart><namePart type="family">De Raedt</namePart><affiliation>Department of Computer Science, Albert-Ludwigs University Freiburg, Georges Köhler-Allee, Geb. 079, 79110, Freiburg, Germany</affiliation><affiliation>E-mail: deraedt@informatik.uni-freiburg.de</affiliation><role><roleTerm type="text">editor</roleTerm></role></name><name type="personal"><namePart type="given">Peter</namePart><namePart type="family">Flach</namePart><affiliation>Department of Computer Science, University of Bristol, Merchant Ventures Bldg., Woodland Road, BS8 1UB, Bristol, UK</affiliation><affiliation>E-mail: peter.flach@bristol.ac.uk</affiliation><role><roleTerm type="text">editor</roleTerm></role></name><genre type="Book Series" displayLabel="Proceedings"></genre><originInfo><copyrightDate encoding="w3cdtf">2001</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="I21017">Artificial Intelligence (incl. 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