{"id":"https://openalex.org/W1998174406","doi":"https://doi.org/10.1109/ijcnn.2014.6889757","title":"Learning features with structure-adapting multi-view exponential family harmoniums","display_name":"Learning features with structure-adapting multi-view exponential family harmoniums","publication_year":2014,"publication_date":"2014-07-01","ids":{"openalex":"https://openalex.org/W1998174406","doi":"https://doi.org/10.1109/ijcnn.2014.6889757","mag":"1998174406"},"language":"en","primary_location":{"id":"doi:10.1109/ijcnn.2014.6889757","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn.2014.6889757","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2014 International Joint Conference on Neural Networks (IJCNN)","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5102412574","display_name":"Yoonseop Kang","orcid":null},"institutions":[{"id":"https://openalex.org/I123900574","display_name":"Pohang University of Science and Technology","ror":"https://ror.org/04xysgw12","country_code":"KR","type":"education","lineage":["https://openalex.org/I123900574"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Yoonseop Kang","raw_affiliation_strings":["Department of Computer Science and Engineering, Pohang University of Science and Technology, Pohang, Korea","Dept. of Computer Science and Engineering, Pohang University of Science and Technology, Pohang, South Korea"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Computer Science and Engineering, Pohang University of Science and Technology, Pohang, Korea","institution_ids":["https://openalex.org/I123900574"]},{"raw_affiliation_string":"Dept. of Computer Science and Engineering, Pohang University of Science and Technology, Pohang, South Korea","institution_ids":["https://openalex.org/I123900574"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5013769424","display_name":"Taewoong Jang","orcid":null},"institutions":[{"id":"https://openalex.org/I123900574","display_name":"Pohang University of Science and Technology","ror":"https://ror.org/04xysgw12","country_code":"KR","type":"education","lineage":["https://openalex.org/I123900574"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Taewoong Jang","raw_affiliation_strings":["Department of Computer Science and Engineering, Pohang University of Science and Technology, Pohang, Korea","Dept. of Computer Science and Engineering, Pohang University of Science and Technology, Pohang, South Korea"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Computer Science and Engineering, Pohang University of Science and Technology, Pohang, Korea","institution_ids":["https://openalex.org/I123900574"]},{"raw_affiliation_string":"Dept. of Computer Science and Engineering, Pohang University of Science and Technology, Pohang, South Korea","institution_ids":["https://openalex.org/I123900574"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5101795797","display_name":"Seungjin Choi","orcid":"https://orcid.org/0000-0002-8301-3776"},"institutions":[{"id":"https://openalex.org/I123900574","display_name":"Pohang University of Science and Technology","ror":"https://ror.org/04xysgw12","country_code":"KR","type":"education","lineage":["https://openalex.org/I123900574"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Seungjin Choi","raw_affiliation_strings":["Department of Computer Science and Engineering, Pohang University of Science and Technology, Pohang, Korea","Dept. of Computer Science and Engineering, Pohang University of Science and Technology, Pohang, South Korea"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Computer Science and Engineering, Pohang University of Science and Technology, Pohang, Korea","institution_ids":["https://openalex.org/I123900574"]},{"raw_affiliation_string":"Dept. of Computer Science and Engineering, Pohang University of Science and Technology, Pohang, South Korea","institution_ids":["https://openalex.org/I123900574"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I123900574"],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":"24","issue":null,"first_page":"2978","last_page":"2985"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10688","display_name":"Image and Signal Denoising Methods","score":0.9961000084877014,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10688","display_name":"Image and Signal Denoising Methods","score":0.9961000084877014,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.9955000281333923,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10500","display_name":"Sparse and Compressive Sensing Techniques","score":0.993399977684021,"subfield":{"id":"https://openalex.org/subfields/2206","display_name":"Computational Mechanics"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7842041254043579},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.6943210363388062},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.6545085906982422},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.6117711663246155},{"id":"https://openalex.org/keywords/margin","display_name":"Margin (machine learning)","score":0.573836088180542},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5430343747138977},{"id":"https://openalex.org/keywords/connection","display_name":"Connection (principal bundle)","score":0.5422769784927368},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5120995044708252},{"id":"https://openalex.org/keywords/feature-vector","display_name":"Feature vector","score":0.502417802810669},{"id":"https://openalex.org/keywords/exponential-function","display_name":"Exponential function","score":0.4907294511795044},{"id":"https://openalex.org/keywords/exponential-family","display_name":"Exponential family","score":0.48936980962753296},{"id":"https://openalex.org/keywords/node","display_name":"Node (physics)","score":0.4704548120498657},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.449118435382843},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.34182512760162354},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.33273524045944214},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.12936490774154663}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7842041254043579},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.6943210363388062},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.6545085906982422},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.6117711663246155},{"id":"https://openalex.org/C774472","wikidata":"https://www.wikidata.org/wiki/Q6760393","display_name":"Margin (machine learning)","level":2,"score":0.573836088180542},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5430343747138977},{"id":"https://openalex.org/C13355873","wikidata":"https://www.wikidata.org/wiki/Q2920850","display_name":"Connection (principal bundle)","level":2,"score":0.5422769784927368},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5120995044708252},{"id":"https://openalex.org/C83665646","wikidata":"https://www.wikidata.org/wiki/Q42139305","display_name":"Feature vector","level":2,"score":0.502417802810669},{"id":"https://openalex.org/C151376022","wikidata":"https://www.wikidata.org/wiki/Q168698","display_name":"Exponential function","level":2,"score":0.4907294511795044},{"id":"https://openalex.org/C55974624","wikidata":"https://www.wikidata.org/wiki/Q1188504","display_name":"Exponential family","level":2,"score":0.48936980962753296},{"id":"https://openalex.org/C62611344","wikidata":"https://www.wikidata.org/wiki/Q1062658","display_name":"Node (physics)","level":2,"score":0.4704548120498657},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.449118435382843},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.34182512760162354},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.33273524045944214},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.12936490774154663},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C199539241","wikidata":"https://www.wikidata.org/wiki/Q7748","display_name":"Law","level":1,"score":0.0},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.0},{"id":"https://openalex.org/C17744445","wikidata":"https://www.wikidata.org/wiki/Q36442","display_name":"Political science","level":0,"score":0.0},{"id":"https://openalex.org/C66938386","wikidata":"https://www.wikidata.org/wiki/Q633538","display_name":"Structural engineering","level":1,"score":0.0},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0},{"id":"https://openalex.org/C94625758","wikidata":"https://www.wikidata.org/wiki/Q7163","display_name":"Politics","level":2,"score":0.0},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ijcnn.2014.6889757","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn.2014.6889757","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2014 International Joint Conference on Neural Networks (IJCNN)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":24,"referenced_works":["https://openalex.org/W1515966130","https://openalex.org/W1576445103","https://openalex.org/W1663973292","https://openalex.org/W1676494021","https://openalex.org/W2040216630","https://openalex.org/W2048679005","https://openalex.org/W2098045186","https://openalex.org/W2100235303","https://openalex.org/W2105464873","https://openalex.org/W2116064496","https://openalex.org/W2124914669","https://openalex.org/W2162315509","https://openalex.org/W2162762921","https://openalex.org/W2295125894","https://openalex.org/W3099514962","https://openalex.org/W3118608800","https://openalex.org/W4212863985","https://openalex.org/W4250857377","https://openalex.org/W6634343353","https://openalex.org/W6674732681","https://openalex.org/W6678801095","https://openalex.org/W6683885849","https://openalex.org/W6697212559","https://openalex.org/W6787972765"],"related_works":["https://openalex.org/W587735977","https://openalex.org/W1978142926","https://openalex.org/W3125011624","https://openalex.org/W1508631387","https://openalex.org/W2370917603","https://openalex.org/W3157395178","https://openalex.org/W2952760143","https://openalex.org/W2017776670","https://openalex.org/W2347897961","https://openalex.org/W2083129711"],"abstract_inverted_index":{"Existing":[0],"multi-view":[1,50,54,148],"feature":[2,14,37,55,149],"extraction":[3,150],"methods":[4],"are":[5,76],"based":[6],"on":[7,10,32,132],"restrictive":[8],"assumptions":[9,20],"the":[11,22,63,79,90,123,138,142,146],"connections":[12],"between":[13,94],"vectors":[15],"and":[16,27,97,134],"input":[17],"data.":[18],"These":[19],"damage":[21],"quality":[23],"of":[24,36,82,92,112,141],"learned":[25],"features,":[26],"also":[28,118],"require":[29],"more":[30],"effort":[31],"choosing":[33],"right":[34],"dimensions":[35],"vector":[38],"components":[39],"connected":[40],"to":[41,65,78,88,108,145],"each":[42,59],"view.":[43],"In":[44],"this":[45],"paper":[46],"we":[47],"present":[48],"adaptive":[49],"harmonium":[51],"(SA-MVH)":[52],"for":[53,128],"extraction,":[56],"where":[57],"its":[58,106],"hidden":[60,95],"node":[61],"chooses":[62],"views":[64],"connect":[66],"with":[67],"while":[68],"training":[69,125],"phase":[70],"via":[71],"switch":[72,100],"parameters.":[73],"\"Switch\"":[74],"parameters":[75],"multiplied":[77],"connection":[80,93],"weights":[81],"ordinary":[83],"exponential":[84],"family":[85],"harmoniums":[86],"(EFH)":[87],"decide":[89],"existence":[91],"nodes":[96],"views.":[98],"With":[99],"parameters,":[101],"a":[102],"SA-MVH":[103],"automatically":[104],"adapts":[105],"structure":[107],"achieve":[109],"better":[110],"representation":[111],"data":[113],"distribution.":[114],"The":[115],"model":[116],"can":[117],"be":[119],"easily":[120],"trained":[121],"using":[122],"same":[124],"algorithms":[126],"used":[127],"EFHs.":[129],"Numerical":[130],"experiments":[131],"synthetic":[133],"real-world":[135],"datasets":[136],"demonstrate":[137],"useful":[139],"behavior":[140],"SA-MVH,":[143],"compared":[144],"existing":[147],"methods.":[151]},"counts_by_year":[],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}
