Sparse support vector machine for pattern recognition
Summary Support vector machine (SVM) is one of the most popular classification techniques in pattern recognition community. However, because of outliers in the training samples, SVM tends to perform poorly under such circumstances. In this paper, we borrow the idea from compressive sensing by introd...
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| Veröffentlicht in: | Concurrency and computation 2016-05, Vol.28 (7), p.2261-2273 |
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| container_title | Concurrency and computation |
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| creator | Chen, Guangyi Bui, Tien. D. Krzyżak, Adam |
| description | Summary
Support vector machine (SVM) is one of the most popular classification techniques in pattern recognition community. However, because of outliers in the training samples, SVM tends to perform poorly under such circumstances. In this paper, we borrow the idea from compressive sensing by introducing an extra term to the objective function of the standard SVM in order to achieve a sparse representation. Furthermore, instead of using the l0 norm, we adopt the l1 norm in our sparse SVM. In most cases, our method achieves higher classification rates than the standard SVM because of sparser support vectors and is more robust to outliers in the datasets. Experimental results show that our proposed SVM is efficient in pattern recognition applications. Copyright © 2015 John Wiley & Sons, Ltd. |
| doi_str_mv | 10.1002/cpe.3492 |
| format | Article |
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Support vector machine (SVM) is one of the most popular classification techniques in pattern recognition community. However, because of outliers in the training samples, SVM tends to perform poorly under such circumstances. In this paper, we borrow the idea from compressive sensing by introducing an extra term to the objective function of the standard SVM in order to achieve a sparse representation. Furthermore, instead of using the l0 norm, we adopt the l1 norm in our sparse SVM. In most cases, our method achieves higher classification rates than the standard SVM because of sparser support vectors and is more robust to outliers in the datasets. Experimental results show that our proposed SVM is efficient in pattern recognition applications. Copyright © 2015 John Wiley & Sons, Ltd.</description><identifier>ISSN: 1532-0626</identifier><identifier>EISSN: 1532-0634</identifier><identifier>DOI: 10.1002/cpe.3492</identifier><language>eng</language><publisher>Blackwell Publishing Ltd</publisher><subject>Classification ; Communities ; image processing ; machine learning ; Mathematical analysis ; Norms ; Outliers (statistics) ; Pattern recognition ; sparse support vector machines ; Support vector machines</subject><ispartof>Concurrency and computation, 2016-05, Vol.28 (7), p.2261-2273</ispartof><rights>Copyright © 2015 John Wiley & Sons, Ltd.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c3702-cb7c11cef0f82a2d10561d2d621d112ed86e11244f13b072291702eb8daffa013</citedby><cites>FETCH-LOGICAL-c3702-cb7c11cef0f82a2d10561d2d621d112ed86e11244f13b072291702eb8daffa013</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://onlinelibrary.wiley.com/doi/pdf/10.1002%2Fcpe.3492$$EPDF$$P50$$Gwiley$$H</linktopdf><linktohtml>$$Uhttps://onlinelibrary.wiley.com/doi/full/10.1002%2Fcpe.3492$$EHTML$$P50$$Gwiley$$H</linktohtml><link.rule.ids>314,776,780,1411,27903,27904,45553,45554</link.rule.ids></links><search><creatorcontrib>Chen, Guangyi</creatorcontrib><creatorcontrib>Bui, Tien. D.</creatorcontrib><creatorcontrib>Krzyżak, Adam</creatorcontrib><title>Sparse support vector machine for pattern recognition</title><title>Concurrency and computation</title><addtitle>Concurrency Computat.: Pract. Exper</addtitle><description>Summary
Support vector machine (SVM) is one of the most popular classification techniques in pattern recognition community. However, because of outliers in the training samples, SVM tends to perform poorly under such circumstances. In this paper, we borrow the idea from compressive sensing by introducing an extra term to the objective function of the standard SVM in order to achieve a sparse representation. Furthermore, instead of using the l0 norm, we adopt the l1 norm in our sparse SVM. In most cases, our method achieves higher classification rates than the standard SVM because of sparser support vectors and is more robust to outliers in the datasets. Experimental results show that our proposed SVM is efficient in pattern recognition applications. Copyright © 2015 John Wiley & Sons, Ltd.</description><subject>Classification</subject><subject>Communities</subject><subject>image processing</subject><subject>machine learning</subject><subject>Mathematical analysis</subject><subject>Norms</subject><subject>Outliers (statistics)</subject><subject>Pattern recognition</subject><subject>sparse support vector machines</subject><subject>Support vector machines</subject><issn>1532-0626</issn><issn>1532-0634</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2016</creationdate><recordtype>article</recordtype><recordid>eNp10D9PwzAQBXALgUQpSHyEjCwpd3acpCOqSkFUgMRfsViuc4ZAmgTbBfrtSVVUxMD03vC7Gx5jhwgDBODHpqWBSIZ8i_VQCh5DKpLtTefpLtvz_hUAEQT2mLxptfMU-UXbNi5EH2RC46K5Ni9lTZHteqtDIFdHjkzzXJehbOp9tmN15engJ_vs7nR8OzqLp1eT89HJNDYiAx6bWWYQDVmwOde8QJApFrxIORaInIo8pS6TxKKYQcb5ELszmuWFtlYDij47Wv9tXfO-IB_UvPSGqkrX1Cy8whxySCUM5S81rvHekVWtK-faLRWCWi2jumXUapmOxmv6WVa0_Nep0fX4ry99oK-N1-5NpZnIpHq4nCjxlMuLx-xe3YpvXLhyxQ</recordid><startdate>201605</startdate><enddate>201605</enddate><creator>Chen, Guangyi</creator><creator>Bui, Tien. D.</creator><creator>Krzyżak, Adam</creator><general>Blackwell Publishing Ltd</general><scope>BSCLL</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7SC</scope><scope>8FD</scope><scope>JQ2</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope></search><sort><creationdate>201605</creationdate><title>Sparse support vector machine for pattern recognition</title><author>Chen, Guangyi ; Bui, Tien. D. ; Krzyżak, Adam</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c3702-cb7c11cef0f82a2d10561d2d621d112ed86e11244f13b072291702eb8daffa013</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2016</creationdate><topic>Classification</topic><topic>Communities</topic><topic>image processing</topic><topic>machine learning</topic><topic>Mathematical analysis</topic><topic>Norms</topic><topic>Outliers (statistics)</topic><topic>Pattern recognition</topic><topic>sparse support vector machines</topic><topic>Support vector machines</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Chen, Guangyi</creatorcontrib><creatorcontrib>Bui, Tien. D.</creatorcontrib><creatorcontrib>Krzyżak, Adam</creatorcontrib><collection>Istex</collection><collection>CrossRef</collection><collection>Computer and Information Systems Abstracts</collection><collection>Technology Research Database</collection><collection>ProQuest Computer Science Collection</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computer and Information Systems Abstracts Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><jtitle>Concurrency and computation</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Chen, Guangyi</au><au>Bui, Tien. D.</au><au>Krzyżak, Adam</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Sparse support vector machine for pattern recognition</atitle><jtitle>Concurrency and computation</jtitle><addtitle>Concurrency Computat.: Pract. Exper</addtitle><date>2016-05</date><risdate>2016</risdate><volume>28</volume><issue>7</issue><spage>2261</spage><epage>2273</epage><pages>2261-2273</pages><issn>1532-0626</issn><eissn>1532-0634</eissn><abstract>Summary
Support vector machine (SVM) is one of the most popular classification techniques in pattern recognition community. However, because of outliers in the training samples, SVM tends to perform poorly under such circumstances. In this paper, we borrow the idea from compressive sensing by introducing an extra term to the objective function of the standard SVM in order to achieve a sparse representation. Furthermore, instead of using the l0 norm, we adopt the l1 norm in our sparse SVM. In most cases, our method achieves higher classification rates than the standard SVM because of sparser support vectors and is more robust to outliers in the datasets. Experimental results show that our proposed SVM is efficient in pattern recognition applications. Copyright © 2015 John Wiley & Sons, Ltd.</abstract><pub>Blackwell Publishing Ltd</pub><doi>10.1002/cpe.3492</doi><tpages>13</tpages><oa>free_for_read</oa></addata></record> |
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| language | eng |
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| source | Wiley Online Library Journals Frontfile Complete |
| subjects | Classification Communities image processing machine learning Mathematical analysis Norms Outliers (statistics) Pattern recognition sparse support vector machines Support vector machines |
| title | Sparse support vector machine for pattern recognition |
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