Mixture weight influence on kernel entropy component analysis and semi-supervised learning using the Lasso
The aim of this paper is two-fold. First, we show that the newly developed spectral method known as kernel entropy component analysis (kernel ECA) captures cluster structure, which is very important in semi-supervised learning, and we provide an analysis showing how mixture weights influence kernel...
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| creator | Myhre, J. N. Jenssen, R. |
| description | The aim of this paper is two-fold. First, we show that the newly developed spectral method known as kernel entropy component analysis (kernel ECA) captures cluster structure, which is very important in semi-supervised learning, and we provide an analysis showing how mixture weights influence kernel ECA in a mixture of cluster components setting. Second, we develop a semi-supervised kernel ECA classifier based on the Lasso framework, and report promising results compared to the state-of-the art. |
| doi_str_mv | 10.1109/MLSP.2012.6349814 |
| format | Conference Proceeding |
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Second, we develop a semi-supervised kernel ECA classifier based on the Lasso framework, and report promising results compared to the state-of-the art.</description><subject>Cluster assumption</subject><subject>Clustering algorithms</subject><subject>Convolution</subject><subject>Data spectroscopy</subject><subject>Eigenvalues and eigenfunctions</subject><subject>Entropy</subject><subject>Heart</subject><subject>Kernel</subject><subject>Kernel entropy component analysis</subject><subject>Lasso</subject><subject>Mixture densities</subject><subject>Semi-supervised learning</subject><subject>Spectral dimensionality reduction</subject><subject>Vectors</subject><issn>1551-2541</issn><issn>2378-928X</issn><isbn>1467310247</isbn><isbn>9781467310246</isbn><isbn>9781467310260</isbn><isbn>1467310263</isbn><isbn>9781467310253</isbn><isbn>1467310255</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2012</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><sourceid>RIE</sourceid><recordid>eNo1UMlOwzAUNJtEKf0AxMU_kOLn3UdUsUmpQAIkbpWbvLQuqVPFCdC_JwiYwywaaQ5DyAWwKQBzV_P8-WnKGfCpFtJZkAdk4syg2ghgXLNDMuLC2Mxx-3ZEzv4LaY7JCJSCjCsJp2SS0oYNsOCkhhHZzMNX17dIPzGs1h0Nsap7jAXSJtJ3bCPWFGPXNrs9LZrtrolDoj76ep9CGkxJE25Dlvodth8hYUlr9G0McUX79MPdGmnuU2rOyUnl64STPx2T19ubl9l9lj_ePcyu8yyAUV2ml4xbVxpZ2KUswSyNMkJ45UoBleAlSuGNtk5r4WVlrPaMFcoBV4VkvCzEmFz-7gZEXOzasPXtfvF3m_gGHbld9g</recordid><startdate>201209</startdate><enddate>201209</enddate><creator>Myhre, J. 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N. ; Jenssen, R.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-i175t-6b0289d74c8b4d17b75733a59d31f32de43a7689663a4f786a00c59125c402dc3</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2012</creationdate><topic>Cluster assumption</topic><topic>Clustering algorithms</topic><topic>Convolution</topic><topic>Data spectroscopy</topic><topic>Eigenvalues and eigenfunctions</topic><topic>Entropy</topic><topic>Heart</topic><topic>Kernel</topic><topic>Kernel entropy component analysis</topic><topic>Lasso</topic><topic>Mixture densities</topic><topic>Semi-supervised learning</topic><topic>Spectral dimensionality reduction</topic><topic>Vectors</topic><toplevel>online_resources</toplevel><creatorcontrib>Myhre, J. N.</creatorcontrib><creatorcontrib>Jenssen, R.</creatorcontrib><collection>IEEE Electronic Library (IEL) Conference Proceedings</collection><collection>IEEE Proceedings Order Plan All Online (POP All Online) 1998-present by volume</collection><collection>IEEE Xplore All Conference Proceedings</collection><collection>IEEE Electronic Library (IEL)</collection><collection>IEEE Proceedings Order Plans (POP All) 1998-Present</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Myhre, J. N.</au><au>Jenssen, R.</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>Mixture weight influence on kernel entropy component analysis and semi-supervised learning using the Lasso</atitle><btitle>2012 IEEE International Workshop on Machine Learning for Signal Processing</btitle><stitle>MLSP</stitle><date>2012-09</date><risdate>2012</risdate><spage>1</spage><epage>6</epage><pages>1-6</pages><issn>1551-2541</issn><eissn>2378-928X</eissn><isbn>1467310247</isbn><isbn>9781467310246</isbn><eisbn>9781467310260</eisbn><eisbn>1467310263</eisbn><eisbn>9781467310253</eisbn><eisbn>1467310255</eisbn><abstract>The aim of this paper is two-fold. First, we show that the newly developed spectral method known as kernel entropy component analysis (kernel ECA) captures cluster structure, which is very important in semi-supervised learning, and we provide an analysis showing how mixture weights influence kernel ECA in a mixture of cluster components setting. 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| ispartof | 2012 IEEE International Workshop on Machine Learning for Signal Processing, 2012, p.1-6 |
| issn | 1551-2541 2378-928X |
| language | eng |
| recordid | cdi_ieee_primary_6349814 |
| source | IEEE Electronic Library (IEL) Conference Proceedings |
| subjects | Cluster assumption Clustering algorithms Convolution Data spectroscopy Eigenvalues and eigenfunctions Entropy Heart Kernel Kernel entropy component analysis Lasso Mixture densities Semi-supervised learning Spectral dimensionality reduction Vectors |
| title | Mixture weight influence on kernel entropy component analysis and semi-supervised learning using the Lasso |
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