A union of incoherent spaces model for classification
We present a new and computationally efficient scheme for classifying signals into a fixed number of known classes. We model classes as subspaces in which the corresponding data is well represented by a dictionary of features. In order to ensure low misclassification, the subspaces should be incoher...
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| creator | Schnass, K Vandergheynst, P |
| description | We present a new and computationally efficient scheme for classifying signals into a fixed number of known classes. We model classes as subspaces in which the corresponding data is well represented by a dictionary of features. In order to ensure low misclassification, the subspaces should be incoherent so that features of a given class cannot represent efficiently signals from another. We propose a simple iterative strategy to learn dictionaries which are are the same time good for approximating within a class and also discriminant. Preliminary tests on a standard face images database show competitive results. |
| doi_str_mv | 10.1109/ICASSP.2010.5495208 |
| format | Conference Proceeding |
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Preliminary tests on a standard face images database show competitive results.</description><subject>alternate projections</subject><subject>classification</subject><subject>Dictionaries</subject><subject>dictionary learning</subject><subject>feature selection</subject><subject>Grassmannian manifolds</subject><subject>Image databases</subject><subject>Laboratories</subject><subject>Signal processing</subject><subject>Space technology</subject><subject>subspace learning</subject><subject>Testing</subject><subject>Training data</subject><issn>1520-6149</issn><issn>2379-190X</issn><isbn>9781424442959</isbn><isbn>1424442958</isbn><isbn>9781424442966</isbn><isbn>1424442966</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2010</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><sourceid>RIE</sourceid><recordid>eNpVkEtLA0EQhMcXGGN-QS5zl43z6tnpYwi-IKAQBW9LO-klI5vdsBMF_70LCYKngqqvmqaEmGo101rh7dNivlq9zIwaDHAIRoUTMcEyaGeccwa9PxUjY0ssNKr3s38Z4LkY6aFTeO3wUlzl_KmUCqULIwFz-dWmrpVdLVMbuw333O5l3lHkLLfdmhtZd72MDeWc6hRpP9DX4qKmJvPkqGPxdn_3ungsls8Pw6_LYmPA7wtvfazBuWhxvcZAJXnzESDYSNHGSF4rqKHkgM4CA1tGzy6Sth5KYGfH4uZwd0NNtevTlvqfqqNUPc6XVWr7RJVS4ENA-NYDPT3QiZn_8ONe9hfVhVku</recordid><startdate>20100101</startdate><enddate>20100101</enddate><creator>Schnass, K</creator><creator>Vandergheynst, P</creator><general>IEEE</general><scope>6IE</scope><scope>6IH</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIO</scope><scope>1XC</scope></search><sort><creationdate>20100101</creationdate><title>A union of incoherent spaces model for classification</title><author>Schnass, K ; Vandergheynst, P</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-h256t-636cf544c39dd98a7a62b8583cac3cca6105f57e89435e5e3e96e4ca136575e43</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2010</creationdate><topic>alternate projections</topic><topic>classification</topic><topic>Dictionaries</topic><topic>dictionary learning</topic><topic>feature selection</topic><topic>Grassmannian manifolds</topic><topic>Image databases</topic><topic>Laboratories</topic><topic>Signal processing</topic><topic>Space technology</topic><topic>subspace learning</topic><topic>Testing</topic><topic>Training data</topic><toplevel>online_resources</toplevel><creatorcontrib>Schnass, K</creatorcontrib><creatorcontrib>Vandergheynst, P</creatorcontrib><collection>IEEE Electronic Library (IEL) Conference Proceedings</collection><collection>IEEE Proceedings Order Plan (POP) 1998-present by volume</collection><collection>IEEE Xplore All Conference Proceedings</collection><collection>IEEE Electronic Library (IEL)</collection><collection>IEEE Proceedings Order Plans (POP) 1998-present</collection><collection>Hyper Article en Ligne (HAL)</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Schnass, K</au><au>Vandergheynst, P</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>A union of incoherent spaces model for classification</atitle><btitle>2010 IEEE International Conference on Acoustics, Speech and Signal Processing</btitle><stitle>ICASSP</stitle><date>2010-01-01</date><risdate>2010</risdate><spage>5490</spage><epage>5493</epage><pages>5490-5493</pages><issn>1520-6149</issn><eissn>2379-190X</eissn><isbn>9781424442959</isbn><isbn>1424442958</isbn><eisbn>9781424442966</eisbn><eisbn>1424442966</eisbn><abstract>We present a new and computationally efficient scheme for classifying signals into a fixed number of known classes. 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| source | IEEE Electronic Library (IEL) Conference Proceedings |
| subjects | alternate projections classification Dictionaries dictionary learning feature selection Grassmannian manifolds Image databases Laboratories Signal processing Space technology subspace learning Testing Training data |
| title | A union of incoherent spaces model for classification |
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