Independent component analysis using Potts models
We explore the extending application of Potts encoding to the task of independent component analysis, which primarily deals with the problem of minimizing the Kullback-Leibler divergence between the joint distribution and the product of all marginal distributions of output components. The competitiv...
Gespeichert in:
| Veröffentlicht in: | IEEE transaction on neural networks and learning systems 2001-03, Vol.12 (2), p.202-211 |
|---|---|
| Hauptverfasser: | , |
| Format: | Artikel |
| Sprache: | eng |
| Schlagworte: | |
| Online-Zugang: | Volltext bestellen |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| container_end_page | 211 |
|---|---|
| container_issue | 2 |
| container_start_page | 202 |
| container_title | IEEE transaction on neural networks and learning systems |
| container_volume | 12 |
| creator | Wu, J M Chiu, S J |
| description | We explore the extending application of Potts encoding to the task of independent component analysis, which primarily deals with the problem of minimizing the Kullback-Leibler divergence between the joint distribution and the product of all marginal distributions of output components. The competitive mechanism of Potts neurons is used to encode the overlapping projections from observations to output components. Based on these projections, the marginal distributions and the entropy of output components are made tractable for computation and the adaptation of the de-mixing matrix toward independent output components is obtained. The Potts model for ICA is well formulated by an objective function subject to a set of constraints, which leads to a novel energy function. A hybrid of the mean field annealing and the gradient descent method is applied to the energy function. Our approach to independent component analysis presents a new criterion for ICA. The performance of the Potts model for ICA given by our numerical simulations is encouraging. |
| doi_str_mv | 10.1109/72.914518 |
| format | Article |
| fullrecord | <record><control><sourceid>proquest_RIE</sourceid><recordid>TN_cdi_proquest_miscellaneous_733453995</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><ieee_id>914518</ieee_id><sourcerecordid>28434359</sourcerecordid><originalsourceid>FETCH-LOGICAL-c366t-752abbb8a3854a66f79cf5190d5db383934082b84f0b2560686be8a33a7fec093</originalsourceid><addsrcrecordid>eNqF0b9LxDAUB_AgineeDq4OcjgoDj1ffiejHP44ONBB55K0qfToL5t2uP_elBYFB12SB_nky0seQucYVhiDvpNkpTHjWB2gOdYMRwCaHoYaGI80IXKGTrzfAQQE4hjNsCKMUanmCG-q1DUuLFW3TOqyqauhMpUp9j73y97n1cfyte46vyzr1BX-FB1lpvDubNoX6P3x4W39HG1fnjbr-22UUCG6SHJirLXKUMWZESKTOsk41pDy1FJFNWWgiFUsA0u4AKGEdUFTIzOXhP4X6GbMbdr6s3e-i8vcJ64oTOXq3sfhxYIBVfhfKSllnGrNg7z-UxLFKKNc_w-F4lypIfHqF9zVfRt-LzRIQAlJYUC3I0ra2vvWZXHT5qVp9zGGeJhgLEk8TjDYyymwt6VLf-Q0sgAuRpA7576Pp9tfzhaa6g</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>920867305</pqid></control><display><type>article</type><title>Independent component analysis using Potts models</title><source>IEEE Electronic Library (IEL)</source><creator>Wu, J M ; Chiu, S J</creator><creatorcontrib>Wu, J M ; Chiu, S J</creatorcontrib><description>We explore the extending application of Potts encoding to the task of independent component analysis, which primarily deals with the problem of minimizing the Kullback-Leibler divergence between the joint distribution and the product of all marginal distributions of output components. The competitive mechanism of Potts neurons is used to encode the overlapping projections from observations to output components. Based on these projections, the marginal distributions and the entropy of output components are made tractable for computation and the adaptation of the de-mixing matrix toward independent output components is obtained. The Potts model for ICA is well formulated by an objective function subject to a set of constraints, which leads to a novel energy function. A hybrid of the mean field annealing and the gradient descent method is applied to the energy function. Our approach to independent component analysis presents a new criterion for ICA. The performance of the Potts model for ICA given by our numerical simulations is encouraging.</description><identifier>ISSN: 1045-9227</identifier><identifier>ISSN: 2162-237X</identifier><identifier>EISSN: 1941-0093</identifier><identifier>EISSN: 2162-2388</identifier><identifier>DOI: 10.1109/72.914518</identifier><identifier>PMID: 18244378</identifier><identifier>CODEN: ITNNEP</identifier><language>eng</language><publisher>United States: IEEE</publisher><subject>Annealing ; Computer simulation ; Distributed computing ; Encoding ; Entropy ; Equations ; Independent component analysis ; Mathematical analysis ; Mathematical models ; Neural networks ; Neurons ; Numerical simulation ; Projection ; Speech analysis ; Studies ; Tasks ; Unsupervised learning</subject><ispartof>IEEE transaction on neural networks and learning systems, 2001-03, Vol.12 (2), p.202-211</ispartof><rights>Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2001</rights><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c366t-752abbb8a3854a66f79cf5190d5db383934082b84f0b2560686be8a33a7fec093</citedby><cites>FETCH-LOGICAL-c366t-752abbb8a3854a66f79cf5190d5db383934082b84f0b2560686be8a33a7fec093</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/914518$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>314,780,784,796,27924,27925,54758</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/914518$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/18244378$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Wu, J M</creatorcontrib><creatorcontrib>Chiu, S J</creatorcontrib><title>Independent component analysis using Potts models</title><title>IEEE transaction on neural networks and learning systems</title><addtitle>TNN</addtitle><addtitle>IEEE Trans Neural Netw</addtitle><description>We explore the extending application of Potts encoding to the task of independent component analysis, which primarily deals with the problem of minimizing the Kullback-Leibler divergence between the joint distribution and the product of all marginal distributions of output components. The competitive mechanism of Potts neurons is used to encode the overlapping projections from observations to output components. Based on these projections, the marginal distributions and the entropy of output components are made tractable for computation and the adaptation of the de-mixing matrix toward independent output components is obtained. The Potts model for ICA is well formulated by an objective function subject to a set of constraints, which leads to a novel energy function. A hybrid of the mean field annealing and the gradient descent method is applied to the energy function. Our approach to independent component analysis presents a new criterion for ICA. The performance of the Potts model for ICA given by our numerical simulations is encouraging.</description><subject>Annealing</subject><subject>Computer simulation</subject><subject>Distributed computing</subject><subject>Encoding</subject><subject>Entropy</subject><subject>Equations</subject><subject>Independent component analysis</subject><subject>Mathematical analysis</subject><subject>Mathematical models</subject><subject>Neural networks</subject><subject>Neurons</subject><subject>Numerical simulation</subject><subject>Projection</subject><subject>Speech analysis</subject><subject>Studies</subject><subject>Tasks</subject><subject>Unsupervised learning</subject><issn>1045-9227</issn><issn>2162-237X</issn><issn>1941-0093</issn><issn>2162-2388</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2001</creationdate><recordtype>article</recordtype><sourceid>RIE</sourceid><recordid>eNqF0b9LxDAUB_AgineeDq4OcjgoDj1ffiejHP44ONBB55K0qfToL5t2uP_elBYFB12SB_nky0seQucYVhiDvpNkpTHjWB2gOdYMRwCaHoYaGI80IXKGTrzfAQQE4hjNsCKMUanmCG-q1DUuLFW3TOqyqauhMpUp9j73y97n1cfyte46vyzr1BX-FB1lpvDubNoX6P3x4W39HG1fnjbr-22UUCG6SHJirLXKUMWZESKTOsk41pDy1FJFNWWgiFUsA0u4AKGEdUFTIzOXhP4X6GbMbdr6s3e-i8vcJ64oTOXq3sfhxYIBVfhfKSllnGrNg7z-UxLFKKNc_w-F4lypIfHqF9zVfRt-LzRIQAlJYUC3I0ra2vvWZXHT5qVp9zGGeJhgLEk8TjDYyymwt6VLf-Q0sgAuRpA7576Pp9tfzhaa6g</recordid><startdate>200103</startdate><enddate>200103</enddate><creator>Wu, J M</creator><creator>Chiu, S J</creator><general>IEEE</general><general>The Institute of Electrical and Electronics Engineers, Inc. (IEEE)</general><scope>RIA</scope><scope>RIE</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7QF</scope><scope>7QO</scope><scope>7QP</scope><scope>7QQ</scope><scope>7QR</scope><scope>7SC</scope><scope>7SE</scope><scope>7SP</scope><scope>7SR</scope><scope>7TA</scope><scope>7TB</scope><scope>7TK</scope><scope>7U5</scope><scope>8BQ</scope><scope>8FD</scope><scope>F28</scope><scope>FR3</scope><scope>H8D</scope><scope>JG9</scope><scope>JQ2</scope><scope>KR7</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope><scope>P64</scope><scope>7X8</scope></search><sort><creationdate>200103</creationdate><title>Independent component analysis using Potts models</title><author>Wu, J M ; Chiu, S J</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c366t-752abbb8a3854a66f79cf5190d5db383934082b84f0b2560686be8a33a7fec093</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2001</creationdate><topic>Annealing</topic><topic>Computer simulation</topic><topic>Distributed computing</topic><topic>Encoding</topic><topic>Entropy</topic><topic>Equations</topic><topic>Independent component analysis</topic><topic>Mathematical analysis</topic><topic>Mathematical models</topic><topic>Neural networks</topic><topic>Neurons</topic><topic>Numerical simulation</topic><topic>Projection</topic><topic>Speech analysis</topic><topic>Studies</topic><topic>Tasks</topic><topic>Unsupervised learning</topic><toplevel>online_resources</toplevel><creatorcontrib>Wu, J M</creatorcontrib><creatorcontrib>Chiu, S J</creatorcontrib><collection>IEEE All-Society Periodicals Package (ASPP) 1998-Present</collection><collection>IEEE Electronic Library (IEL)</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>Aluminium Industry Abstracts</collection><collection>Biotechnology Research Abstracts</collection><collection>Calcium & Calcified Tissue Abstracts</collection><collection>Ceramic Abstracts</collection><collection>Chemoreception Abstracts</collection><collection>Computer and Information Systems Abstracts</collection><collection>Corrosion Abstracts</collection><collection>Electronics & Communications Abstracts</collection><collection>Engineered Materials Abstracts</collection><collection>Materials Business File</collection><collection>Mechanical & Transportation Engineering Abstracts</collection><collection>Neurosciences Abstracts</collection><collection>Solid State and Superconductivity Abstracts</collection><collection>METADEX</collection><collection>Technology Research Database</collection><collection>ANTE: Abstracts in New Technology & Engineering</collection><collection>Engineering Research Database</collection><collection>Aerospace Database</collection><collection>Materials Research Database</collection><collection>ProQuest Computer Science Collection</collection><collection>Civil Engineering Abstracts</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computer and Information Systems Abstracts Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><collection>Biotechnology and BioEngineering Abstracts</collection><collection>MEDLINE - Academic</collection><jtitle>IEEE transaction on neural networks and learning systems</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Wu, J M</au><au>Chiu, S J</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Independent component analysis using Potts models</atitle><jtitle>IEEE transaction on neural networks and learning systems</jtitle><stitle>TNN</stitle><addtitle>IEEE Trans Neural Netw</addtitle><date>2001-03</date><risdate>2001</risdate><volume>12</volume><issue>2</issue><spage>202</spage><epage>211</epage><pages>202-211</pages><issn>1045-9227</issn><issn>2162-237X</issn><eissn>1941-0093</eissn><eissn>2162-2388</eissn><coden>ITNNEP</coden><abstract>We explore the extending application of Potts encoding to the task of independent component analysis, which primarily deals with the problem of minimizing the Kullback-Leibler divergence between the joint distribution and the product of all marginal distributions of output components. The competitive mechanism of Potts neurons is used to encode the overlapping projections from observations to output components. Based on these projections, the marginal distributions and the entropy of output components are made tractable for computation and the adaptation of the de-mixing matrix toward independent output components is obtained. The Potts model for ICA is well formulated by an objective function subject to a set of constraints, which leads to a novel energy function. A hybrid of the mean field annealing and the gradient descent method is applied to the energy function. Our approach to independent component analysis presents a new criterion for ICA. The performance of the Potts model for ICA given by our numerical simulations is encouraging.</abstract><cop>United States</cop><pub>IEEE</pub><pmid>18244378</pmid><doi>10.1109/72.914518</doi><tpages>10</tpages></addata></record> |
| fulltext | fulltext_linktorsrc |
| identifier | ISSN: 1045-9227 |
| ispartof | IEEE transaction on neural networks and learning systems, 2001-03, Vol.12 (2), p.202-211 |
| issn | 1045-9227 2162-237X 1941-0093 2162-2388 |
| language | eng |
| recordid | cdi_proquest_miscellaneous_733453995 |
| source | IEEE Electronic Library (IEL) |
| subjects | Annealing Computer simulation Distributed computing Encoding Entropy Equations Independent component analysis Mathematical analysis Mathematical models Neural networks Neurons Numerical simulation Projection Speech analysis Studies Tasks Unsupervised learning |
| title | Independent component analysis using Potts models |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2024-12-28T14%3A02%3A08IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_RIE&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Independent%20component%20analysis%20using%20Potts%20models&rft.jtitle=IEEE%20transaction%20on%20neural%20networks%20and%20learning%20systems&rft.au=Wu,%20J%20M&rft.date=2001-03&rft.volume=12&rft.issue=2&rft.spage=202&rft.epage=211&rft.pages=202-211&rft.issn=1045-9227&rft.eissn=1941-0093&rft.coden=ITNNEP&rft_id=info:doi/10.1109/72.914518&rft_dat=%3Cproquest_RIE%3E28434359%3C/proquest_RIE%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=920867305&rft_id=info:pmid/18244378&rft_ieee_id=914518&rfr_iscdi=true |