Margin Maximizing Discriminant Analysis
Identifieur interne : 001442 ( Main/Exploration ); précédent : 001441; suivant : 001443Margin Maximizing Discriminant Analysis
Auteurs : András Kocsor [Hongrie] ; Kornél Kovács [Hongrie] ; Csaba Szepesvári [Hongrie]Source :
- Lecture Notes in Computer Science [ 0302-9743 ] ; 2004.
Abstract
Abstract: We propose a new feature extraction method called Margin Maximizing Discriminant Analysis (MMDA) which seeks to extract features suitable for classification tasks. MMDA is based on the principle that an ideal feature should convey the maximum information about the class labels and it should depend only on the geometry of the optimal decision boundary and not on those parts of the distribution of the input data that do not participate in shaping this boundary. Further, distinct feature components should convey unrelated information about the data. Two feature extraction methods are proposed for calculating the parameters of such a projection that are shown to yield equivalent results. The kernel mapping idea is used to derive non-linear versions. Experiments with several real-world, publicly available data sets demonstrate that the new method yields competitive results.
Url:
DOI: 10.1007/978-3-540-30115-8_23
Affiliations:
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<record><TEI wicri:istexFullTextTei="biblStruct:series"><teiHeader><fileDesc><titleStmt><title xml:lang="en">Margin Maximizing Discriminant Analysis</title><author><name sortKey="Kocsor, Andras" sort="Kocsor, Andras" uniqKey="Kocsor A" first="András" last="Kocsor">András Kocsor</name></author><author><name sortKey="Kovacs, Kornel" sort="Kovacs, Kornel" uniqKey="Kovacs K" first="Kornél" last="Kovács">Kornél Kovács</name></author><author><name sortKey="Szepesvari, Csaba" sort="Szepesvari, Csaba" uniqKey="Szepesvari C" first="Csaba" last="Szepesvári">Csaba Szepesvári</name></author></titleStmt><publicationStmt><idno type="wicri:source">ISTEX</idno><idno type="RBID">ISTEX:BC59E13042DE8429CD1265D5B0DCBFAC120AE0FA</idno><date when="2004" year="2004">2004</date><idno type="doi">10.1007/978-3-540-30115-8_23</idno><idno type="url">https://api.istex.fr/document/BC59E13042DE8429CD1265D5B0DCBFAC120AE0FA/fulltext/pdf</idno><idno type="wicri:Area/Istex/Corpus">002A95</idno><idno type="wicri:Area/Istex/Curation">002883</idno><idno type="wicri:Area/Istex/Checkpoint">000C77</idno><idno type="wicri:doubleKey">0302-9743:2004:Kocsor A:margin:maximizing:discriminant</idno><idno type="wicri:Area/Main/Merge">001493</idno><idno type="wicri:Area/Main/Curation">001442</idno><idno type="wicri:Area/Main/Exploration">001442</idno></publicationStmt><sourceDesc><biblStruct><analytic><title level="a" type="main" xml:lang="en">Margin Maximizing Discriminant Analysis</title><author><name sortKey="Kocsor, Andras" sort="Kocsor, Andras" uniqKey="Kocsor A" first="András" last="Kocsor">András Kocsor</name><affiliation wicri:level="1"><country xml:lang="fr">Hongrie</country><wicri:regionArea>Research Group on Artificial Intelligence of the Hungarian Academy of Sciences, University of Szeged, Aradi vértanúk tere 1, 6720, Szeged</wicri:regionArea><wicri:noRegion>Szeged</wicri:noRegion></affiliation><affiliation wicri:level="1"><country wicri:rule="url">Hongrie</country></affiliation></author><author><name sortKey="Kovacs, Kornel" sort="Kovacs, Kornel" uniqKey="Kovacs K" first="Kornél" last="Kovács">Kornél Kovács</name><affiliation wicri:level="1"><country xml:lang="fr">Hongrie</country><wicri:regionArea>Research Group on Artificial Intelligence of the Hungarian Academy of Sciences, University of Szeged, Aradi vértanúk tere 1, 6720, Szeged</wicri:regionArea><wicri:noRegion>Szeged</wicri:noRegion></affiliation><affiliation wicri:level="1"><country wicri:rule="url">Hongrie</country></affiliation></author><author><name sortKey="Szepesvari, Csaba" sort="Szepesvari, Csaba" uniqKey="Szepesvari C" first="Csaba" last="Szepesvári">Csaba Szepesvári</name><affiliation wicri:level="3"><country xml:lang="fr">Hongrie</country><wicri:regionArea>Computer and Automation Research Institute of the Hungarian Academy of Sciences, Kende u. 13-17, 1111, Budapest</wicri:regionArea><placeName><settlement type="city">Budapest</settlement><region nuts="2">Hongrie centrale</region></placeName></affiliation><affiliation wicri:level="1"><country wicri:rule="url">Hongrie</country></affiliation></author></analytic><monogr></monogr><series><title level="s">Lecture Notes in Computer Science</title><imprint><date>2004</date></imprint><idno type="ISSN">0302-9743</idno><idno type="eISSN">1611-3349</idno><idno type="ISSN">0302-9743</idno></series><idno type="istex">BC59E13042DE8429CD1265D5B0DCBFAC120AE0FA</idno><idno type="DOI">10.1007/978-3-540-30115-8_23</idno><idno type="ChapterID">23</idno><idno type="ChapterID">Chap23</idno></biblStruct></sourceDesc><seriesStmt><idno type="ISSN">0302-9743</idno></seriesStmt></fileDesc><profileDesc><textClass></textClass><langUsage><language ident="en">en</language></langUsage></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">Abstract: We propose a new feature extraction method called Margin Maximizing Discriminant Analysis (MMDA) which seeks to extract features suitable for classification tasks. MMDA is based on the principle that an ideal feature should convey the maximum information about the class labels and it should depend only on the geometry of the optimal decision boundary and not on those parts of the distribution of the input data that do not participate in shaping this boundary. Further, distinct feature components should convey unrelated information about the data. Two feature extraction methods are proposed for calculating the parameters of such a projection that are shown to yield equivalent results. The kernel mapping idea is used to derive non-linear versions. Experiments with several real-world, publicly available data sets demonstrate that the new method yields competitive results.</div></front></TEI><affiliations><list><country><li>Hongrie</li></country><region><li>Hongrie centrale</li></region><settlement><li>Budapest</li></settlement></list><tree><country name="Hongrie"><noRegion><name sortKey="Kocsor, Andras" sort="Kocsor, Andras" uniqKey="Kocsor A" first="András" last="Kocsor">András Kocsor</name></noRegion><name sortKey="Kocsor, Andras" sort="Kocsor, Andras" uniqKey="Kocsor A" first="András" last="Kocsor">András Kocsor</name><name sortKey="Kovacs, Kornel" sort="Kovacs, Kornel" uniqKey="Kovacs K" first="Kornél" last="Kovács">Kornél Kovács</name><name sortKey="Kovacs, Kornel" sort="Kovacs, Kornel" uniqKey="Kovacs K" first="Kornél" last="Kovács">Kornél Kovács</name><name sortKey="Szepesvari, Csaba" sort="Szepesvari, Csaba" uniqKey="Szepesvari C" first="Csaba" last="Szepesvári">Csaba Szepesvári</name><name sortKey="Szepesvari, Csaba" sort="Szepesvari, Csaba" uniqKey="Szepesvari C" first="Csaba" last="Szepesvári">Csaba Szepesvári</name></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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