{"id":"https://openalex.org/W4416583107","doi":"https://doi.org/10.1109/dsaa65442.2025.11248027","title":"Nonnegative Matrix Factorization and the Principle of the Common Cause","display_name":"Nonnegative Matrix Factorization and the Principle of the Common Cause","publication_year":2025,"publication_date":"2025-10-09","ids":{"openalex":"https://openalex.org/W4416583107","doi":"https://doi.org/10.1109/dsaa65442.2025.11248027"},"language":null,"primary_location":{"id":"doi:10.1109/dsaa65442.2025.11248027","is_oa":false,"landing_page_url":"https://doi.org/10.1109/dsaa65442.2025.11248027","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE 12th International Conference on Data Science and Advanced Analytics (DSAA)","raw_type":"proceedings-article"},"type":"article","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/A5011544501","display_name":"Edvard Khalafyan","orcid":"https://orcid.org/0009-0007-9330-1695"},"institutions":[{"id":"https://openalex.org/I153845743","display_name":"Moscow Institute of Physics and Technology","ror":"https://ror.org/00v0z9322","country_code":"RU","type":"education","lineage":["https://openalex.org/I153845743"]}],"countries":["RU"],"is_corresponding":true,"raw_author_name":"Edvard Khalafyan","raw_affiliation_strings":["Moscow Institute of Physics and Technology,Moscow,Russia"],"affiliations":[{"raw_affiliation_string":"Moscow Institute of Physics and Technology,Moscow,Russia","institution_ids":["https://openalex.org/I153845743"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5054875221","display_name":"A. E. Allahverdyan","orcid":"https://orcid.org/0000-0003-2542-3504"},"institutions":[{"id":"https://openalex.org/I132091411","display_name":"A. Alikhanyan National Laboratory","ror":"https://ror.org/00ad27c73","country_code":"AM","type":"facility","lineage":["https://openalex.org/I132091411"]}],"countries":["AM"],"is_corresponding":false,"raw_author_name":"Armen Allahverdyan","raw_affiliation_strings":["Alikhanyan National Laboratory,Yerevan,Armenia"],"affiliations":[{"raw_affiliation_string":"Alikhanyan National Laboratory,Yerevan,Armenia","institution_ids":["https://openalex.org/I132091411"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5102795686","display_name":"A. A. Hovhannisyan","orcid":"https://orcid.org/0000-0001-8723-0455"},"institutions":[{"id":"https://openalex.org/I132091411","display_name":"A. Alikhanyan National Laboratory","ror":"https://ror.org/00ad27c73","country_code":"AM","type":"facility","lineage":["https://openalex.org/I132091411"]}],"countries":["AM"],"is_corresponding":false,"raw_author_name":"Arshak Hovhannisyan","raw_affiliation_strings":["Alikhanyan National Laboratory,Yerevan,Armenia"],"affiliations":[{"raw_affiliation_string":"Alikhanyan National Laboratory,Yerevan,Armenia","institution_ids":["https://openalex.org/I132091411"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5011544501"],"corresponding_institution_ids":["https://openalex.org/I153845743"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.39188532,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"10"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10057","display_name":"Face and Expression Recognition","score":0.19130000472068787,"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/T10057","display_name":"Face and Expression Recognition","score":0.19130000472068787,"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/T10775","display_name":"Generative Adversarial Networks and Image Synthesis","score":0.13199999928474426,"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/T12303","display_name":"Tensor decomposition and applications","score":0.08669999986886978,"subfield":{"id":"https://openalex.org/subfields/2605","display_name":"Computational Mathematics"},"field":{"id":"https://openalex.org/fields/26","display_name":"Mathematics"},"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":0.8579999804496765},{"id":"https://openalex.org/keywords/cluster-analysis","display_name":"Cluster analysis","score":0.5842999815940857},{"id":"https://openalex.org/keywords/rank","display_name":"Rank (graph theory)","score":0.583899974822998},{"id":"https://openalex.org/keywords/matrix-decomposition","display_name":"Matrix decomposition","score":0.5133000016212463},{"id":"https://openalex.org/keywords/probabilistic-logic","display_name":"Probabilistic logic","score":0.5131999850273132},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.5048999786376953},{"id":"https://openalex.org/keywords/factorization","display_name":"Factorization","score":0.47110000252723694},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4228000044822693},{"id":"https://openalex.org/keywords/joint-probability-distribution","display_name":"Joint probability distribution","score":0.41510000824928284}],"concepts":[{"id":"https://openalex.org/C152671427","wikidata":"https://www.wikidata.org/wiki/Q10843505","display_name":"Non-negative matrix factorization","level":4,"score":0.8579999804496765},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.6079000234603882},{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.5842999815940857},{"id":"https://openalex.org/C164226766","wikidata":"https://www.wikidata.org/wiki/Q7293202","display_name":"Rank (graph theory)","level":2,"score":0.583899974822998},{"id":"https://openalex.org/C42355184","wikidata":"https://www.wikidata.org/wiki/Q1361088","display_name":"Matrix decomposition","level":3,"score":0.5133000016212463},{"id":"https://openalex.org/C49937458","wikidata":"https://www.wikidata.org/wiki/Q2599292","display_name":"Probabilistic logic","level":2,"score":0.5131999850273132},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.5048999786376953},{"id":"https://openalex.org/C187834632","wikidata":"https://www.wikidata.org/wiki/Q188804","display_name":"Factorization","level":2,"score":0.47110000252723694},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4228000044822693},{"id":"https://openalex.org/C18653775","wikidata":"https://www.wikidata.org/wiki/Q1333358","display_name":"Joint probability distribution","level":2,"score":0.41510000824928284},{"id":"https://openalex.org/C61224824","wikidata":"https://www.wikidata.org/wiki/Q2260434","display_name":"Mixture model","level":2,"score":0.40869998931884766},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.38269999623298645},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.37459999322891235},{"id":"https://openalex.org/C106487976","wikidata":"https://www.wikidata.org/wiki/Q685816","display_name":"Matrix (chemical analysis)","level":2,"score":0.3693000078201294},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.3384999930858612},{"id":"https://openalex.org/C90199385","wikidata":"https://www.wikidata.org/wiki/Q6692777","display_name":"Low-rank approximation","level":3,"score":0.3337000012397766},{"id":"https://openalex.org/C122123141","wikidata":"https://www.wikidata.org/wiki/Q176623","display_name":"Random variable","level":2,"score":0.3319999873638153},{"id":"https://openalex.org/C197640229","wikidata":"https://www.wikidata.org/wiki/Q2534066","display_name":"Predictability","level":2,"score":0.32989999651908875},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.328900009393692},{"id":"https://openalex.org/C103278499","wikidata":"https://www.wikidata.org/wiki/Q254465","display_name":"Similarity (geometry)","level":3,"score":0.30309998989105225},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.2937000095844269},{"id":"https://openalex.org/C114289077","wikidata":"https://www.wikidata.org/wiki/Q3284399","display_name":"Statistical model","level":2,"score":0.28139999508857727},{"id":"https://openalex.org/C18555067","wikidata":"https://www.wikidata.org/wiki/Q8375051","display_name":"Joint (building)","level":2,"score":0.2809000015258789},{"id":"https://openalex.org/C56372850","wikidata":"https://www.wikidata.org/wiki/Q1050404","display_name":"Sparse matrix","level":3,"score":0.2709999978542328},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.257999986410141},{"id":"https://openalex.org/C160234255","wikidata":"https://www.wikidata.org/wiki/Q812535","display_name":"Bayesian inference","level":3,"score":0.2572999894618988},{"id":"https://openalex.org/C149441793","wikidata":"https://www.wikidata.org/wiki/Q200726","display_name":"Probability distribution","level":2,"score":0.2565999925136566}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/dsaa65442.2025.11248027","is_oa":false,"landing_page_url":"https://doi.org/10.1109/dsaa65442.2025.11248027","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE 12th International Conference on Data Science and Advanced Analytics (DSAA)","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":25,"referenced_works":["https://openalex.org/W1814500939","https://openalex.org/W1902027874","https://openalex.org/W2008411291","https://openalex.org/W2014297169","https://openalex.org/W2018718896","https://openalex.org/W2059745395","https://openalex.org/W2085937140","https://openalex.org/W2101234009","https://openalex.org/W2102578078","https://openalex.org/W2124172487","https://openalex.org/W2132692097","https://openalex.org/W2152557171","https://openalex.org/W2496518686","https://openalex.org/W2578613799","https://openalex.org/W2592232824","https://openalex.org/W2604274631","https://openalex.org/W2618107892","https://openalex.org/W2888097834","https://openalex.org/W2972158684","https://openalex.org/W3003257820","https://openalex.org/W3026981332","https://openalex.org/W3114089334","https://openalex.org/W3155575086","https://openalex.org/W3212172675","https://openalex.org/W4401073968"],"related_works":[],"abstract_inverted_index":{"Nonnegative":[0],"matrix":[1],"factorization":[2],"(NMF)":[3],"is":[4,17,52,104],"a":[5,18,73,79,176],"known":[6],"unsupervised":[7],"data-reduction":[8],"method.":[9],"The":[10],"principle":[11],"of":[12,35,58,82,86,101,130,149],"the":[13,32,83,95,102,136,141,169,183,190],"common":[14,185],"cause":[15,186],"(PCC)":[16],"basic":[19],"methodological":[20],"approach":[21],"in":[22,152],"probabilistic":[23],"causality,":[24],"which":[25,61],"seeks":[26],"an":[27,146,153],"independent":[28,170],"mixture":[29,171],"model":[30],"for":[31,55,201],"joint":[33,161],"probability":[34,66],"two":[36,45],"dependent":[37],"random":[38],"variables.":[39],"It":[40],"turns":[41],"out":[42,175],"that":[43,76,111,121],"these":[44],"concepts":[46],"are":[47,62,122,187],"closely":[48],"related.":[49],"This":[50],"relationship":[51],"explored":[53],"reciprocally":[54],"several":[56],"datasets":[57],"gray-scale":[59],"images,":[60],"conveniently":[63],"mapped":[64],"into":[65,189],"models.":[67],"On":[68,140],"one":[69],"hand,":[70,143],"PCC":[71,151],"provides":[72,145],"predictability":[74],"tool":[75],"leads":[77],"to":[78,164],"robust":[80],"estimation":[81],"effective":[84],"rank":[85,103,116],"NMF.":[87],"Unlike":[88],"other":[89,142],"estimates":[90],"(e.g.,":[91],"those":[92],"based":[93],"on":[94],"Bayesian":[96],"Information":[97],"Criteria),":[98],"our":[99],"estimate":[100],"stable":[105,124],"against":[106,125,128],"weak":[107],"noise.":[108],"We":[109,173,193],"show":[110,195],"NMF":[112,137,144,197],"implemented":[113],"around":[114],"this":[115],"produces":[117],"features":[118],"(basis":[119],"images)":[120],"also":[123,194],"noise":[126],"and":[127,158],"seeds":[129],"local":[131],"optimization,":[132],"thereby":[133],"effectively":[134],"resolving":[135],"nonidentifiability":[138],"problem.":[139],"interesting":[147],"possibility":[148],"implementing":[150],"approximate":[154],"way,":[155],"where":[156,179],"larger":[157],"positively":[159],"correlated":[160],"probabilities":[162],"tend":[163],"be":[165,199],"explained":[166],"better":[167],"via":[168],"model.":[172],"work":[174],"clustering":[177],"method,":[178],"data":[180,202],"points":[181],"with":[182],"same":[184,191],"grouped":[188],"cluster.":[192],"how":[196],"can":[198],"employed":[200],"denoising.":[203]},"counts_by_year":[],"updated_date":"2026-03-07T16:01:11.037858","created_date":"2025-11-25T00:00:00"}
