{"id":"https://openalex.org/W1970685459","doi":"https://doi.org/10.1145/2671188.2749316","title":"Distribution Regularized Nonnegative Matrix Factorization for Transfer Visual Feature Learning","display_name":"Distribution Regularized Nonnegative Matrix Factorization for Transfer Visual Feature Learning","publication_year":2015,"publication_date":"2015-06-22","ids":{"openalex":"https://openalex.org/W1970685459","doi":"https://doi.org/10.1145/2671188.2749316","mag":"1970685459"},"language":"en","primary_location":{"id":"doi:10.1145/2671188.2749316","is_oa":false,"landing_page_url":"https://doi.org/10.1145/2671188.2749316","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 5th ACM on International Conference on Multimedia Retrieval","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/A5035519525","display_name":"Yuchen Guo","orcid":"https://orcid.org/0000-0001-9808-9805"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yuchen Guo","raw_affiliation_strings":["Tsinghua University, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Tsinghua University, Beijing, China","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5057732142","display_name":"Guiguang Ding","orcid":"https://orcid.org/0000-0003-0137-9975"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Guiguang Ding","raw_affiliation_strings":["Tsinghua University, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Tsinghua University, Beijing, China","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5057648803","display_name":"Qiang Liu","orcid":"https://orcid.org/0000-0003-1037-2185"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Qiang Liu","raw_affiliation_strings":["Tsinghua University, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Tsinghua University, Beijing, China","institution_ids":["https://openalex.org/I99065089"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I99065089"],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"299","last_page":"306"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.9994999766349792,"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"}},"topics":[{"id":"https://openalex.org/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.9994999766349792,"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/T10627","display_name":"Advanced Image and Video Retrieval Techniques","score":0.9782000184059143,"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/T10689","display_name":"Remote-Sensing Image Classification","score":0.9437999725341797,"subfield":{"id":"https://openalex.org/subfields/2214","display_name":"Media Technology"},"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/non-negative-matrix-factorization","display_name":"Non-negative matrix factorization","score":0.6969202756881714},{"id":"https://openalex.org/keywords/marginal-distribution","display_name":"Marginal distribution","score":0.6256590485572815},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6190584301948547},{"id":"https://openalex.org/keywords/conditional-probability-distribution","display_name":"Conditional probability distribution","score":0.5977420210838318},{"id":"https://openalex.org/keywords/matrix-decomposition","display_name":"Matrix decomposition","score":0.5438114404678345},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5320219993591309},{"id":"https://openalex.org/keywords/probability-distribution","display_name":"Probability distribution","score":0.5241377949714661},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.5061034560203552},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.49461838603019714},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4687250852584839},{"id":"https://openalex.org/keywords/transfer-of-learning","display_name":"Transfer of learning","score":0.46443116664886475},{"id":"https://openalex.org/keywords/feature-vector","display_name":"Feature vector","score":0.4387502074241638},{"id":"https://openalex.org/keywords/contextual-image-classification","display_name":"Contextual image classification","score":0.4189624786376953},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.34870341420173645},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.32340699434280396},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.30501240491867065},{"id":"https://openalex.org/keywords/eigenvalues-and-eigenvectors","display_name":"Eigenvalues and eigenvectors","score":0.08839729428291321},{"id":"https://openalex.org/keywords/random-variable","display_name":"Random variable","score":0.08150133490562439}],"concepts":[{"id":"https://openalex.org/C152671427","wikidata":"https://www.wikidata.org/wiki/Q10843505","display_name":"Non-negative matrix factorization","level":4,"score":0.6969202756881714},{"id":"https://openalex.org/C165216359","wikidata":"https://www.wikidata.org/wiki/Q670653","display_name":"Marginal distribution","level":3,"score":0.6256590485572815},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6190584301948547},{"id":"https://openalex.org/C43555835","wikidata":"https://www.wikidata.org/wiki/Q2300258","display_name":"Conditional probability distribution","level":2,"score":0.5977420210838318},{"id":"https://openalex.org/C42355184","wikidata":"https://www.wikidata.org/wiki/Q1361088","display_name":"Matrix decomposition","level":3,"score":0.5438114404678345},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5320219993591309},{"id":"https://openalex.org/C149441793","wikidata":"https://www.wikidata.org/wiki/Q200726","display_name":"Probability distribution","level":2,"score":0.5241377949714661},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.5061034560203552},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.49461838603019714},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4687250852584839},{"id":"https://openalex.org/C150899416","wikidata":"https://www.wikidata.org/wiki/Q1820378","display_name":"Transfer of learning","level":2,"score":0.46443116664886475},{"id":"https://openalex.org/C83665646","wikidata":"https://www.wikidata.org/wiki/Q42139305","display_name":"Feature vector","level":2,"score":0.4387502074241638},{"id":"https://openalex.org/C75294576","wikidata":"https://www.wikidata.org/wiki/Q5165192","display_name":"Contextual image classification","level":3,"score":0.4189624786376953},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.34870341420173645},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.32340699434280396},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.30501240491867065},{"id":"https://openalex.org/C158693339","wikidata":"https://www.wikidata.org/wiki/Q190524","display_name":"Eigenvalues and eigenvectors","level":2,"score":0.08839729428291321},{"id":"https://openalex.org/C122123141","wikidata":"https://www.wikidata.org/wiki/Q176623","display_name":"Random variable","level":2,"score":0.08150133490562439},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.0},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","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}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/2671188.2749316","is_oa":false,"landing_page_url":"https://doi.org/10.1145/2671188.2749316","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 5th ACM on International Conference on Multimedia Retrieval","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G6201887244","display_name":"\u6570\u636e\u9a71\u52a8\u7684\u5927\u89c4\u6a21\u56fe\u50cf\u81ea\u52a8\u6807\u6ce8\u5173\u952e\u6280\u672f\u7814\u7a76","funder_award_id":"61271394","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":38,"referenced_works":["https://openalex.org/W20696413","https://openalex.org/W1501486674","https://openalex.org/W1784617778","https://openalex.org/W1902027874","https://openalex.org/W1976258951","https://openalex.org/W1988348003","https://openalex.org/W2024687090","https://openalex.org/W2034692798","https://openalex.org/W2043228833","https://openalex.org/W2049633694","https://openalex.org/W2067067957","https://openalex.org/W2069678212","https://openalex.org/W2089685866","https://openalex.org/W2096943734","https://openalex.org/W2107298017","https://openalex.org/W2108119513","https://openalex.org/W2115403315","https://openalex.org/W2118045473","https://openalex.org/W2125865219","https://openalex.org/W2136171036","https://openalex.org/W2138621090","https://openalex.org/W2144730066","https://openalex.org/W2149466042","https://openalex.org/W2149676790","https://openalex.org/W2150846797","https://openalex.org/W2159570078","https://openalex.org/W2161047120","https://openalex.org/W2162854380","https://openalex.org/W2165698076","https://openalex.org/W2604272474","https://openalex.org/W2990138404","https://openalex.org/W2999905431","https://openalex.org/W3023742835","https://openalex.org/W3177525997","https://openalex.org/W6676215431","https://openalex.org/W6677741084","https://openalex.org/W6678814708","https://openalex.org/W7048738093"],"related_works":["https://openalex.org/W4390394189","https://openalex.org/W2037504162","https://openalex.org/W2539013788","https://openalex.org/W2792706544","https://openalex.org/W1568451138","https://openalex.org/W2156699640","https://openalex.org/W2045265907","https://openalex.org/W2972997031","https://openalex.org/W2146544734","https://openalex.org/W2075222291"],"abstract_inverted_index":{"Transfer":[0],"visual":[1,85],"feature":[2],"learning":[3],"(TVFL),":[4],"which":[5,121,169],"learns":[6],"compact":[7],"representations":[8],"for":[9,18,63,127,137],"images":[10],"such":[11],"that":[12],"we":[13,57,73],"can":[14,102],"build":[15],"accurate":[16],"classifier":[17],"target":[19],"domain":[20],"by":[21],"leveraging":[22],"rich":[23],"labeled":[24],"data":[25],"in":[26,52,84,157],"the":[27,41,48,81,104,108,138,145,164],"source":[28],"domain,":[29],"has":[30],"attracted":[31],"increasingly":[32],"attention":[33],"recently.":[34],"Previous":[35],"methods":[36,162],"mainly":[37],"focus":[38],"on":[39,149],"reducing":[40,112],"distribution":[42,95,118],"difference":[43,119],"between":[44],"domains":[45],"but":[46],"ignore":[47],"intrinsic":[49,82,105],"hidden":[50],"semantics":[51],"data.":[53],"In":[54],"this":[55],"paper,":[56],"put":[58],"forward":[59],"a":[60],"novel":[61],"method":[62],"TVFL,":[64],"called":[65],"Distribution":[66],"Regularized":[67],"Nonnegative":[68,75],"Matrix":[69,76],"Factorization":[70,77],"(DRNMF).":[71],"Specifically,":[72],"employ":[74],"(NMF)":[78],"to":[79],"uncover":[80],"information":[83],"data,":[86],"and":[87,96,111,115,134,142],"regularize":[88],"it":[89],"with":[90,159],"geometrical":[91],"distribution,":[92],"marginal":[93,114],"probability":[94,98,117],"conditional":[97,116],"distribution.":[99],"Thus,":[100],"DRNMF":[101,141],"discover":[103],"information,":[106],"preserve":[107],"manifold":[109],"structure":[110],"both":[113],"simultaneously,":[120],"all":[122],"perspectives":[123],"above":[124],"are":[125],"important":[126],"TVFL.":[128],"We":[129],"also":[130],"propose":[131],"an":[132],"effective":[133],"efficient":[135],"algorithm":[136],"optimization":[139],"of":[140,152,166],"theoretically":[143],"prove":[144],"convergence.":[146],"Extensive":[147],"experiments":[148],"three":[150],"types":[151],"cross-domain":[153],"image":[154],"classification":[155],"tasks":[156],"comparison":[158],"several":[160],"state-of-the-art":[161],"demonstrate":[163],"superiority":[165],"our":[167],"DRNMF,":[168],"validates":[170],"its":[171],"effectiveness.":[172]},"counts_by_year":[{"year":2022,"cited_by_count":1},{"year":2019,"cited_by_count":1},{"year":2016,"cited_by_count":1}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}