Multiple Cayley-Klein metric learning
As a specific kind of non-Euclidean metric lies in projective space, Cayley-Klein metric has been recently introduced in metric learning to deal with the complex data distributions in computer vision tasks. In this paper, we extend the original Cayley-Klein metric to the multiple Cayley-Klein metric...
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| Veröffentlicht in: | PloS one 2017-09, Vol.12 (9), p.e0184865-e0184865 |
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| description | As a specific kind of non-Euclidean metric lies in projective space, Cayley-Klein metric has been recently introduced in metric learning to deal with the complex data distributions in computer vision tasks. In this paper, we extend the original Cayley-Klein metric to the multiple Cayley-Klein metric, which is defined as a linear combination of several Cayley-Klein metrics. Since Cayley-Klein is a kind of non-linear metric, its combination could model the data space better, thus lead to an improved performance. We show how to learn a multiple Cayley-Klein metric by iterative optimization over single Cayley-Klein metric and their combination coefficients under the objective to maximize the performance on separating inter-class instances and gathering intra-class instances. Our experiments on several benchmarks are quite encouraging. |
| doi_str_mv | 10.1371/journal.pone.0184865 |
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Our experiments on several benchmarks are quite encouraging.</description><identifier>ISSN: 1932-6203</identifier><identifier>EISSN: 1932-6203</identifier><identifier>DOI: 10.1371/journal.pone.0184865</identifier><identifier>PMID: 28934244</identifier><language>eng</language><publisher>United States: Public Library of Science</publisher><subject>Automation ; Benchmarks ; Biology and Life Sciences ; Classification ; Computer and Information Sciences ; Computer vision ; Datasets as Topic ; Euclidean geometry ; Face ; Geometry ; Humans ; Image Processing, Computer-Assisted - methods ; Internet ; Iterative methods ; Laboratories ; Linear Models ; Machine Learning ; Neighborhoods ; Neural networks ; Nonlinear Dynamics ; Optimization ; Pattern recognition ; Pattern Recognition, Automated - methods ; Physical Sciences ; Research and Analysis Methods ; Researchers ; Social Sciences ; Task complexity ; Teaching methods ; Technology application ; Time Factors</subject><ispartof>PloS one, 2017-09, Vol.12 (9), p.e0184865-e0184865</ispartof><rights>COPYRIGHT 2017 Public Library of Science</rights><rights>2017 Bi et al. This is an open access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><rights>2017 Bi et al 2017 Bi et al</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c692t-d65aaf50f1118b85ad94fe115d530ee55671ab23eae5421f3110af9769cd49883</citedby><cites>FETCH-LOGICAL-c692t-d65aaf50f1118b85ad94fe115d530ee55671ab23eae5421f3110af9769cd49883</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC5608239/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC5608239/$$EHTML$$P50$$Gpubmedcentral$$Hfree_for_read</linktohtml><link.rule.ids>230,314,724,777,781,861,882,2096,2915,23847,27905,27906,53772,53774,79349,79350</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/28934244$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><contributor>Hou, Chenping</contributor><creatorcontrib>Bi, Yanhong</creatorcontrib><creatorcontrib>Fan, Bin</creatorcontrib><creatorcontrib>Wu, Fuchao</creatorcontrib><title>Multiple Cayley-Klein metric learning</title><title>PloS one</title><addtitle>PLoS One</addtitle><description>As a specific kind of non-Euclidean metric lies in projective space, Cayley-Klein metric has been recently introduced in metric learning to deal with the complex data distributions in computer vision tasks. 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Academic</collection><collection>PubMed Central (Full Participant titles)</collection><collection>DOAJ Directory of Open Access Journals</collection><jtitle>PloS one</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Bi, Yanhong</au><au>Fan, Bin</au><au>Wu, Fuchao</au><au>Hou, Chenping</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Multiple Cayley-Klein metric learning</atitle><jtitle>PloS one</jtitle><addtitle>PLoS One</addtitle><date>2017-09-21</date><risdate>2017</risdate><volume>12</volume><issue>9</issue><spage>e0184865</spage><epage>e0184865</epage><pages>e0184865-e0184865</pages><issn>1932-6203</issn><eissn>1932-6203</eissn><abstract>As a specific kind of non-Euclidean metric lies in projective space, Cayley-Klein metric has been recently introduced in metric learning to deal with the complex data distributions in computer vision tasks. In this paper, we extend the original Cayley-Klein metric to the multiple Cayley-Klein metric, which is defined as a linear combination of several Cayley-Klein metrics. Since Cayley-Klein is a kind of non-linear metric, its combination could model the data space better, thus lead to an improved performance. We show how to learn a multiple Cayley-Klein metric by iterative optimization over single Cayley-Klein metric and their combination coefficients under the objective to maximize the performance on separating inter-class instances and gathering intra-class instances. Our experiments on several benchmarks are quite encouraging.</abstract><cop>United States</cop><pub>Public Library of Science</pub><pmid>28934244</pmid><doi>10.1371/journal.pone.0184865</doi><tpages>e0184865</tpages><oa>free_for_read</oa></addata></record> |
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| subjects | Automation Benchmarks Biology and Life Sciences Classification Computer and Information Sciences Computer vision Datasets as Topic Euclidean geometry Face Geometry Humans Image Processing, Computer-Assisted - methods Internet Iterative methods Laboratories Linear Models Machine Learning Neighborhoods Neural networks Nonlinear Dynamics Optimization Pattern recognition Pattern Recognition, Automated - methods Physical Sciences Research and Analysis Methods Researchers Social Sciences Task complexity Teaching methods Technology application Time Factors |
| title | Multiple Cayley-Klein metric learning |
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