{"id":"https://openalex.org/W2794125792","doi":"https://doi.org/10.1145/3162957.3162996","title":"Accuracy of separable nonnegative matrix factorization for topic extraction","display_name":"Accuracy of separable nonnegative matrix factorization for topic extraction","publication_year":2017,"publication_date":"2017-11-24","ids":{"openalex":"https://openalex.org/W2794125792","doi":"https://doi.org/10.1145/3162957.3162996","mag":"2794125792"},"language":"en","primary_location":{"id":"doi:10.1145/3162957.3162996","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3162957.3162996","pdf_url":null,"source":{"id":"https://openalex.org/S4306523808","display_name":"Proceedings of the 3rd International Conference on Communication and Information Processing","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 3rd International Conference on Communication and Information Processing","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/A5052210284","display_name":"Hendri Murfi","orcid":"https://orcid.org/0000-0003-2722-5763"},"institutions":[{"id":"https://openalex.org/I29617571","display_name":"University of Indonesia","ror":"https://ror.org/0116zj450","country_code":"ID","type":"education","lineage":["https://openalex.org/I29617571"]}],"countries":["ID"],"is_corresponding":true,"raw_author_name":"Hendri Murfi","raw_affiliation_strings":["Universitas Indonesia, Depok, Indonesia"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Universitas Indonesia, Depok, Indonesia","institution_ids":["https://openalex.org/I29617571"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5052210284"],"corresponding_institution_ids":["https://openalex.org/I29617571"],"apc_list":null,"apc_paid":null,"fwci":0.5537,"has_fulltext":false,"cited_by_count":7,"citation_normalized_percentile":{"value":0.72183647,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":97},"biblio":{"volume":null,"issue":null,"first_page":"226","last_page":"230"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T13083","display_name":"Advanced Text Analysis Techniques","score":0.9990000128746033,"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/T13083","display_name":"Advanced Text Analysis Techniques","score":0.9990000128746033,"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/T10028","display_name":"Topic Modeling","score":0.9952999949455261,"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/T11550","display_name":"Text and Document Classification Technologies","score":0.9868999719619751,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/non-negative-matrix-factorization","display_name":"Non-negative matrix factorization","score":0.9128677845001221},{"id":"https://openalex.org/keywords/matrix-decomposition","display_name":"Matrix decomposition","score":0.6464865803718567},{"id":"https://openalex.org/keywords/latent-dirichlet-allocation","display_name":"Latent Dirichlet allocation","score":0.6166396141052246},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5480565428733826},{"id":"https://openalex.org/keywords/probabilistic-logic","display_name":"Probabilistic logic","score":0.5342615842819214},{"id":"https://openalex.org/keywords/topic-model","display_name":"Topic model","score":0.5197286605834961},{"id":"https://openalex.org/keywords/matrix","display_name":"Matrix (chemical analysis)","score":0.5143574476242065},{"id":"https://openalex.org/keywords/dirichlet-distribution","display_name":"Dirichlet distribution","score":0.5043777227401733},{"id":"https://openalex.org/keywords/separable-space","display_name":"Separable space","score":0.5040856599807739},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.46917015314102173},{"id":"https://openalex.org/keywords/polynomial","display_name":"Polynomial","score":0.46275565028190613},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3961138129234314},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.3819581866264343},{"id":"https://openalex.org/keywords/eigenvalues-and-eigenvectors","display_name":"Eigenvalues and eigenvectors","score":0.18973973393440247}],"concepts":[{"id":"https://openalex.org/C152671427","wikidata":"https://www.wikidata.org/wiki/Q10843505","display_name":"Non-negative matrix factorization","level":4,"score":0.9128677845001221},{"id":"https://openalex.org/C42355184","wikidata":"https://www.wikidata.org/wiki/Q1361088","display_name":"Matrix decomposition","level":3,"score":0.6464865803718567},{"id":"https://openalex.org/C500882744","wikidata":"https://www.wikidata.org/wiki/Q269236","display_name":"Latent Dirichlet allocation","level":3,"score":0.6166396141052246},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5480565428733826},{"id":"https://openalex.org/C49937458","wikidata":"https://www.wikidata.org/wiki/Q2599292","display_name":"Probabilistic logic","level":2,"score":0.5342615842819214},{"id":"https://openalex.org/C171686336","wikidata":"https://www.wikidata.org/wiki/Q3532085","display_name":"Topic model","level":2,"score":0.5197286605834961},{"id":"https://openalex.org/C106487976","wikidata":"https://www.wikidata.org/wiki/Q685816","display_name":"Matrix (chemical analysis)","level":2,"score":0.5143574476242065},{"id":"https://openalex.org/C169214877","wikidata":"https://www.wikidata.org/wiki/Q981016","display_name":"Dirichlet distribution","level":3,"score":0.5043777227401733},{"id":"https://openalex.org/C70710897","wikidata":"https://www.wikidata.org/wiki/Q680081","display_name":"Separable space","level":2,"score":0.5040856599807739},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.46917015314102173},{"id":"https://openalex.org/C90119067","wikidata":"https://www.wikidata.org/wiki/Q43260","display_name":"Polynomial","level":2,"score":0.46275565028190613},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3961138129234314},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.3819581866264343},{"id":"https://openalex.org/C158693339","wikidata":"https://www.wikidata.org/wiki/Q190524","display_name":"Eigenvalues and eigenvectors","level":2,"score":0.18973973393440247},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C192562407","wikidata":"https://www.wikidata.org/wiki/Q228736","display_name":"Materials science","level":0,"score":0.0},{"id":"https://openalex.org/C159985019","wikidata":"https://www.wikidata.org/wiki/Q181790","display_name":"Composite material","level":1,"score":0.0},{"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/C182310444","wikidata":"https://www.wikidata.org/wiki/Q1332643","display_name":"Boundary value problem","level":2,"score":0.0},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3162957.3162996","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3162957.3162996","pdf_url":null,"source":{"id":"https://openalex.org/S4306523808","display_name":"Proceedings of the 3rd International Conference on Communication and Information Processing","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 3rd International Conference on Communication and Information Processing","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320323819","display_name":"Universitas Indonesia","ror":"https://ror.org/0116zj450"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":19,"referenced_works":["https://openalex.org/W1612003148","https://openalex.org/W1880262756","https://openalex.org/W1982029265","https://openalex.org/W2015583498","https://openalex.org/W2026034143","https://openalex.org/W2063392856","https://openalex.org/W2101234009","https://openalex.org/W2105617746","https://openalex.org/W2110096996","https://openalex.org/W2110558001","https://openalex.org/W2124172487","https://openalex.org/W2128521126","https://openalex.org/W2143017621","https://openalex.org/W2174706414","https://openalex.org/W2601003356","https://openalex.org/W2963625764","https://openalex.org/W2997591727","https://openalex.org/W4251372957","https://openalex.org/W6636440780"],"related_works":["https://openalex.org/W4312773271","https://openalex.org/W4315588616","https://openalex.org/W2769501189","https://openalex.org/W2888805565","https://openalex.org/W2962686197","https://openalex.org/W2207653751","https://openalex.org/W3159709618","https://openalex.org/W2611137333","https://openalex.org/W3005513013","https://openalex.org/W4389543811"],"abstract_inverted_index":{"Topic":[0],"extraction":[1,17],"is":[2,18,24,55,75,131,145],"an":[3],"automatic":[4],"method":[5,14,69],"to":[6,45],"extract":[7],"topics":[8,107],"in":[9,72,82,99,154],"textual":[10],"data.":[11],"The":[12,51],"popular":[13],"of":[15,30,33,119,129,143,157],"topic":[16,27],"latent":[19],"Dirichlet":[20],"allocation":[21],"(LDA)":[22],"which":[23],"a":[25,64,67,87],"probabilistic":[26],"model.":[28],"Because":[29],"some":[31],"limitations":[32],"learning":[34],"the":[35,43,56,100,106,109,117,120,127,135,141,151,163,169],"model":[36],"parameters,":[37],"e.g.":[38],"NP-hard,":[39],"several":[40],"researchers":[41],"continue":[42],"work":[44],"design":[46],"methods":[47],"with":[48],"polynomial":[49,73],"complexities.":[50],"developing":[52],"alternative":[53],"approach":[54],"nonnegative":[57,122],"matrix":[58,123],"factorization":[59,124],"(NMF)":[60],"based":[61],"method.":[62],"Under":[63],"separability":[65],"assumption,":[66],"direct":[68],"that":[70],"runs":[71],"time":[74],"proposed.":[76],"In":[77,112,138],"general,":[78],"this":[79,113,139],"algorithm":[80],"works":[81],"three":[83],"steps:":[84],"first,":[85],"generating":[86],"word":[88],"cooccurrence":[89],"matrix,":[90],"choosing":[91],"anchor":[92,110,136],"words":[93,153],"for":[94],"each":[95],"topic,":[96],"and":[97],"then":[98],"recovery":[101],"step,":[102],"it":[103],"directly":[104],"reconstructs":[105],"given":[108],"words.":[111,137],"paper,":[114],"we":[115,149],"examine":[116],"accuracy":[118,128,142,165,171],"separable":[121],"(SNMF).":[125],"Firstly":[126],"SNMF":[130,144,161],"strongly":[132],"influenced":[133],"by":[134],"case,":[140],"significantly":[146],"improved":[147],"when":[148],"find":[150],"anchr":[152],"Eigenspace,":[155],"instead":[156],"random":[158],"space.":[159],"Moreover,":[160],"gives":[162],"higher":[164],"than":[166,172],"LDA,":[167],"however,":[168],"lower":[170],"NMF.":[173]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":1},{"year":2022,"cited_by_count":1},{"year":2020,"cited_by_count":1},{"year":2019,"cited_by_count":3}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}