{"id":"https://openalex.org/W2580009724","doi":"https://doi.org/10.1109/tsp.2017.2752697","title":"Stochastic Subsampling for Factorizing Huge Matrices","display_name":"Stochastic Subsampling for Factorizing Huge Matrices","publication_year":2017,"publication_date":"2017-09-14","ids":{"openalex":"https://openalex.org/W2580009724","doi":"https://doi.org/10.1109/tsp.2017.2752697","mag":"2580009724"},"language":"en","primary_location":{"id":"doi:10.1109/tsp.2017.2752697","doi":"https://doi.org/10.1109/tsp.2017.2752697","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tsp.2017.2752697","pdf_url":null,"source":{"id":"https://openalex.org/S168680287","display_name":"IEEE Transactions on Signal Processing","issn_l":"1053-587X","issn":["1053-587X","1941-0476"],"is_oa":false,"is_in_doaj":false,"is_core":true,"listed_in":["anvur-scientific","cwts-core","dongbi-a","jufo-3","ki-jl-1","norway-2","poland-140","russia-white-list-1","vabb-shw"],"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Signal Processing","raw_type":"journal-article"},"volume":null,"type":"article","indexed_in":["arxiv","crossref","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/1701.05363","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5061701877","display_name":"Arthur Mensch","orcid":"https://orcid.org/0000-0001-7866-0461","observed_orcids":["https://orcid.org/0000-0001-7866-0461"]},"institutions":[{"id":"https://openalex.org/I2738703131","display_name":"Commissariat \u00e0 l'\u00c9nergie Atomique et aux \u00c9nergies Alternatives","ror":"https://ror.org/00jjx8s55","country_code":"FR","type":"government","lineage":["https://openalex.org/I2738703131"]},{"id":"https://openalex.org/I277688954","display_name":"Universit\u00e9 Paris-Saclay","ror":"https://ror.org/03xjwb503","country_code":"FR","type":"education","lineage":["https://openalex.org/I277688954"]},{"id":"https://openalex.org/I2801360556","display_name":"CEA Paris-Saclay - Etablissement de Saclay","ror":"https://ror.org/01fv25t22","country_code":"FR","type":"facility","lineage":["https://openalex.org/I2738703131","https://openalex.org/I277688954","https://openalex.org/I2801360556","https://openalex.org/I4210128565"]}],"countries":["FR"],"is_corresponding":false,"raw_author_name":"Arthur Mensch","raw_affiliation_strings":["Parietal Team, Paris-Saclay University, Gif-sur-Yvette, France","NEUROSPIN - Service NEUROSPIN (France)","PARIETAL - Modelling brain structure, function and variability based on high-field MRI data (Neurospin, CEA Saclay, B\u00e2timent 145, 91191 Gif-sur-Yvette Cedex - France)"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Parietal Team, Paris-Saclay University, Gif-sur-Yvette, France","institution_ids":["https://openalex.org/I277688954"]},{"raw_affiliation_string":"NEUROSPIN - Service NEUROSPIN (France)","institution_ids":[]},{"raw_affiliation_string":"PARIETAL - Modelling brain structure, function and variability based on high-field MRI data (Neurospin, CEA Saclay, B\u00e2timent 145, 91191 Gif-sur-Yvette Cedex - France)","institution_ids":["https://openalex.org/I2738703131","https://openalex.org/I2801360556"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5062817741","display_name":"Julien Mairal","orcid":"https://orcid.org/0000-0001-6991-2110","observed_orcids":["https://orcid.org/0000-0001-6991-2110"]},"institutions":[{"id":"https://openalex.org/I106785703","display_name":"Institut polytechnique de Grenoble","ror":"https://ror.org/05sbt2524","country_code":"FR","type":"education","lineage":["https://openalex.org/I106785703","https://openalex.org/I899635006"]},{"id":"https://openalex.org/I4210149092","display_name":"Laboratoire Jean Kuntzmann","ror":"https://ror.org/04ett5b41","country_code":"FR","type":"facility","lineage":["https://openalex.org/I106785703","https://openalex.org/I1294671590","https://openalex.org/I1326498283","https://openalex.org/I4210149092","https://openalex.org/I899635006"]},{"id":"https://openalex.org/I4405259482","display_name":"THOTH: Apprentissage de mod\u00e8les visuels \u00e0 partir de donn\u00e9es massives","ror":"https://ror.org/02a2awp29","country_code":"FR","type":"facility","lineage":["https://openalex.org/I106785703","https://openalex.org/I1294671590","https://openalex.org/I1326498283","https://openalex.org/I4210101348","https://openalex.org/I4210149092","https://openalex.org/I4405259482","https://openalex.org/I899635006"]},{"id":"https://openalex.org/I899635006","display_name":"Universit\u00e9 Grenoble Alpes","ror":"https://ror.org/02rx3b187","country_code":"FR","type":"education","lineage":["https://openalex.org/I899635006"]}],"countries":["FR"],"is_corresponding":false,"raw_author_name":"Julien Mairal","raw_affiliation_strings":["Universit\u00e9 Grenoble Alpes, Grenoble INP, LJK, Grenoble, France","Thoth - Apprentissage de mod\u00e8les \u00e0 partir de donn\u00e9es massives (France)"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Universit\u00e9 Grenoble Alpes, Grenoble INP, LJK, Grenoble, France","institution_ids":["https://openalex.org/I106785703","https://openalex.org/I4210149092","https://openalex.org/I899635006"]},{"raw_affiliation_string":"Thoth - Apprentissage de mod\u00e8les \u00e0 partir de donn\u00e9es massives (France)","institution_ids":["https://openalex.org/I4405259482"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5026762833","display_name":"Bertrand Thirion","orcid":"https://orcid.org/0000-0001-5018-7895","observed_orcids":["https://orcid.org/0000-0001-5018-7895"]},"institutions":[{"id":"https://openalex.org/I2738703131","display_name":"Commissariat \u00e0 l'\u00c9nergie Atomique et aux \u00c9nergies Alternatives","ror":"https://ror.org/00jjx8s55","country_code":"FR","type":"government","lineage":["https://openalex.org/I2738703131"]},{"id":"https://openalex.org/I277688954","display_name":"Universit\u00e9 Paris-Saclay","ror":"https://ror.org/03xjwb503","country_code":"FR","type":"education","lineage":["https://openalex.org/I277688954"]},{"id":"https://openalex.org/I2801360556","display_name":"CEA Paris-Saclay - Etablissement de Saclay","ror":"https://ror.org/01fv25t22","country_code":"FR","type":"facility","lineage":["https://openalex.org/I2738703131","https://openalex.org/I277688954","https://openalex.org/I2801360556","https://openalex.org/I4210128565"]}],"countries":["FR"],"is_corresponding":false,"raw_author_name":"Bertrand Thirion","raw_affiliation_strings":["Parietal Team, Paris-Saclay University, Gif-sur-Yvette, France","NEUROSPIN - Service NEUROSPIN (France)","PARIETAL - Modelling brain structure, function and variability based on high-field MRI data (Neurospin, CEA Saclay, B\u00e2timent 145, 91191 Gif-sur-Yvette Cedex - France)"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Parietal Team, Paris-Saclay University, Gif-sur-Yvette, France","institution_ids":["https://openalex.org/I277688954"]},{"raw_affiliation_string":"NEUROSPIN - Service NEUROSPIN (France)","institution_ids":[]},{"raw_affiliation_string":"PARIETAL - Modelling brain structure, function and variability based on high-field MRI data (Neurospin, CEA Saclay, B\u00e2timent 145, 91191 Gif-sur-Yvette Cedex - France)","institution_ids":["https://openalex.org/I2738703131","https://openalex.org/I2801360556"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5074733625","display_name":"Gael P. Varoquaux","orcid":"https://orcid.org/0000-0003-1076-5122","observed_orcids":["https://orcid.org/0000-0003-1076-5122"]},"institutions":[{"id":"https://openalex.org/I2738703131","display_name":"Commissariat \u00e0 l'\u00c9nergie Atomique et aux \u00c9nergies Alternatives","ror":"https://ror.org/00jjx8s55","country_code":"FR","type":"government","lineage":["https://openalex.org/I2738703131"]},{"id":"https://openalex.org/I277688954","display_name":"Universit\u00e9 Paris-Saclay","ror":"https://ror.org/03xjwb503","country_code":"FR","type":"education","lineage":["https://openalex.org/I277688954"]},{"id":"https://openalex.org/I2801360556","display_name":"CEA Paris-Saclay - Etablissement de Saclay","ror":"https://ror.org/01fv25t22","country_code":"FR","type":"facility","lineage":["https://openalex.org/I2738703131","https://openalex.org/I277688954","https://openalex.org/I2801360556","https://openalex.org/I4210128565"]}],"countries":["FR"],"is_corresponding":false,"raw_author_name":"Gael Varoquaux","raw_affiliation_strings":["Parietal Team, Paris-Saclay University, Gif-sur-Yvette, France","NEUROSPIN - Service NEUROSPIN (France)","PARIETAL - Modelling brain structure, function and variability based on high-field MRI data (Neurospin, CEA Saclay, B\u00e2timent 145, 91191 Gif-sur-Yvette Cedex - France)"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Parietal Team, Paris-Saclay University, Gif-sur-Yvette, France","institution_ids":["https://openalex.org/I277688954"]},{"raw_affiliation_string":"NEUROSPIN - Service NEUROSPIN (France)","institution_ids":[]},{"raw_affiliation_string":"PARIETAL - Modelling brain structure, function and variability based on high-field MRI data (Neurospin, CEA Saclay, B\u00e2timent 145, 91191 Gif-sur-Yvette Cedex - France)","institution_ids":["https://openalex.org/I2738703131","https://openalex.org/I2801360556"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":7,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":{"value":2645,"currency":"USD","value_usd":2645},"apc_paid":null,"fwci":4.097,"has_fulltext":false,"cited_by_count":28,"citation_normalized_percentile":{"value":0.93918979,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":89,"max":99},"biblio":{"volume":"66","issue":"1","first_page":"113","last_page":"128"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10500","display_name":"Sparse and Compressive Sensing Techniques","score":0.8633000254631042,"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"}},"topics":[{"id":"https://openalex.org/T10500","display_name":"Sparse and Compressive Sensing Techniques","score":0.8633000254631042,"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"}},{"id":"https://openalex.org/T11612","display_name":"Stochastic Gradient Optimization Techniques","score":0.08669999986886978,"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/T12303","display_name":"Tensor decomposition and applications","score":0.013799999840557575,"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/matrix-factorization","display_name":"matrix factorization","score":0.9919999837875366},{"id":"https://openalex.org/keywords/streaming-algorithms","display_name":"streaming algorithms","score":0.8790000081062317},{"id":"https://openalex.org/keywords/non-negative-matrix-factorization","display_name":"non-negative matrix factorization","score":0.8299999833106995},{"id":"https://openalex.org/keywords/sparse-component-analysis","display_name":"sparse component analysis","score":0.9449999928474426},{"id":"https://openalex.org/keywords/dictionary-learning","display_name":"dictionary learning","score":0.9980000257492065},{"id":"https://openalex.org/keywords/convergence-guarantees","display_name":"convergence guarantees","score":0.8220000267028809},{"id":"https://openalex.org/keywords/critical-points","display_name":"critical points","score":0.6159999966621399},{"id":"https://openalex.org/keywords/fmri","display_name":"functional MRI","score":0.7480000257492065}],"concepts":[{"id":"https://openalex.org/C42355184","wikidata":"https://www.wikidata.org/wiki/Q1361088","display_name":"Matrix decomposition","level":3,"score":0.7029478549957275},{"id":"https://openalex.org/C106487976","wikidata":"https://www.wikidata.org/wiki/Q685816","display_name":"Matrix (chemical analysis)","level":2,"score":0.5746052861213684},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.5663595795631409},{"id":"https://openalex.org/C33676613","wikidata":"https://www.wikidata.org/wiki/Q13415176","display_name":"Dimension (graph theory)","level":2,"score":0.5619907975196838},{"id":"https://openalex.org/C135598885","wikidata":"https://www.wikidata.org/wiki/Q1366302","display_name":"Row","level":2,"score":0.5594666004180908},{"id":"https://openalex.org/C152671427","wikidata":"https://www.wikidata.org/wiki/Q10843505","display_name":"Non-negative matrix factorization","level":4,"score":0.5359379053115845},{"id":"https://openalex.org/C56372850","wikidata":"https://www.wikidata.org/wiki/Q1050404","display_name":"Sparse matrix","level":3,"score":0.5349205136299133},{"id":"https://openalex.org/C104140500","wikidata":"https://www.wikidata.org/wiki/Q2088159","display_name":"Row and column spaces","level":3,"score":0.5338583588600159},{"id":"https://openalex.org/C187834632","wikidata":"https://www.wikidata.org/wiki/Q188804","display_name":"Factorization","level":2,"score":0.5328619480133057},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5215837359428406},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.366754949092865},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3384460210800171},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.21786507964134216},{"id":"https://openalex.org/C114614502","wikidata":"https://www.wikidata.org/wiki/Q76592","display_name":"Combinatorics","level":1,"score":0.18792182207107544},{"id":"https://openalex.org/C158693339","wikidata":"https://www.wikidata.org/wiki/Q190524","display_name":"Eigenvalues and eigenvectors","level":2,"score":0.13161051273345947},{"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/C77088390","wikidata":"https://www.wikidata.org/wiki/Q8513","display_name":"Database","level":1,"score":0.0},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"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/C192562407","wikidata":"https://www.wikidata.org/wiki/Q228736","display_name":"Materials science","level":0,"score":0.0}],"mesh":[],"locations_count":4,"locations":[{"id":"doi:10.1109/tsp.2017.2752697","doi":"https://doi.org/10.1109/tsp.2017.2752697","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tsp.2017.2752697","pdf_url":null,"source":{"id":"https://openalex.org/S168680287","display_name":"IEEE Transactions on Signal Processing","issn_l":"1053-587X","issn":["1053-587X","1941-0476"],"is_oa":false,"is_in_doaj":false,"is_core":true,"listed_in":["anvur-scientific","cwts-core","dongbi-a","jufo-3","ki-jl-1","norway-2","poland-140","russia-white-list-1","vabb-shw"],"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Signal Processing","raw_type":"journal-article"},{"id":"pmh:oai:arXiv.org:1701.05363","doi":null,"is_oa":true,"landing_page_url":"http://arxiv.org/abs/1701.05363","pdf_url":"https://arxiv.org/pdf/1701.05363","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},{"id":"pmh:oai:HAL:hal-01431618v3","doi":null,"is_oa":false,"landing_page_url":"https://hal.science/hal-01431618","pdf_url":null,"source":{"id":"https://openalex.org/S4306402512","display_name":"HAL (Le Centre pour la Communication Scientifique Directe)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I1294671590","host_organization_name":"Centre National de la Recherche Scientifique","host_organization_lineage":["https://openalex.org/I1294671590"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://ieeexplore.ieee.org/document/8038072/","raw_type":"info:eu-repo/semantics/article"},{"id":"doi:10.48550/arxiv.1701.05363","doi":"https://doi.org/10.48550/arxiv.1701.05363","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.1701.05363","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"article-journal"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:1701.05363","doi":null,"is_oa":true,"landing_page_url":"http://arxiv.org/abs/1701.05363","pdf_url":"https://arxiv.org/pdf/1701.05363","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"sustainable_development_goals":[],"sustainable_development_goals_aurora":[],"study_designs":[],"awards":[{"id":"https://openalex.org/G2436814110","display_name":"Outils pour la Recherche Clinique par cartographie de la connectivit\u00e9 c\u00e9r\u00e9brale fonctionnelle","funder_award_id":"ANR-11-BINF-0004","funder_id":"https://openalex.org/F4320320883","funder_display_name":"Agence Nationale de la Recherche"},{"id":"https://openalex.org/G4182220265","display_name":"large-scale machine learning and applications","funder_award_id":"ANR-14-CE23-0003","funder_id":"https://openalex.org/F4320320883","funder_display_name":"Agence Nationale de la Recherche"}],"funders":[{"id":"https://openalex.org/F4320320883","display_name":"Agence Nationale de la Recherche","ror":"https://ror.org/00rbzpz17"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":71,"referenced_works":["https://openalex.org/W20275845","https://openalex.org/W114517082","https://openalex.org/W1509803206","https://openalex.org/W1560723556","https://openalex.org/W1562564909","https://openalex.org/W1731081199","https://openalex.org/W1859506250","https://openalex.org/W1906883763","https://openalex.org/W1974438823","https://openalex.org/W1975900269","https://openalex.org/W1978259121","https://openalex.org/W2000769684","https://openalex.org/W2008732654","https://openalex.org/W2016444985","https://openalex.org/W2023901033","https://openalex.org/W2024729467","https://openalex.org/W2040387238","https://openalex.org/W2045390367","https://openalex.org/W2050968963","https://openalex.org/W2056621966","https://openalex.org/W2056636001","https://openalex.org/W2061212083","https://openalex.org/W2081000321","https://openalex.org/W2089497633","https://openalex.org/W2095906131","https://openalex.org/W2097915756","https://openalex.org/W2101234009","https://openalex.org/W2104266030","https://openalex.org/W2105464873","https://openalex.org/W2105767123","https://openalex.org/W2106833843","https://openalex.org/W2112447569","https://openalex.org/W2117756735","https://openalex.org/W2118718620","https://openalex.org/W2122090912","https://openalex.org/W2122825543","https://openalex.org/W2125031621","https://openalex.org/W2129638195","https://openalex.org/W2135046866","https://openalex.org/W2135921993","https://openalex.org/W2138505091","https://openalex.org/W2146616964","https://openalex.org/W2151591509","https://openalex.org/W2152326788","https://openalex.org/W2165685007","https://openalex.org/W2167167503","https://openalex.org/W2250539671","https://openalex.org/W2319338832","https://openalex.org/W2346689370","https://openalex.org/W2436219157","https://openalex.org/W2611328865","https://openalex.org/W2799137445","https://openalex.org/W2952655975","https://openalex.org/W2962772219","https://openalex.org/W2979473749","https://openalex.org/W3098745759","https://openalex.org/W4206310440","https://openalex.org/W4234117503","https://openalex.org/W4245159036","https://openalex.org/W6633782522","https://openalex.org/W6639093925","https://openalex.org/W6675354045","https://openalex.org/W6676292492","https://openalex.org/W6676628658","https://openalex.org/W6676727762","https://openalex.org/W6677671969","https://openalex.org/W6677759377","https://openalex.org/W6678885109","https://openalex.org/W6679963453","https://openalex.org/W6682597329","https://openalex.org/W6704634208"],"related_works":["https://openalex.org/W2313440505","https://openalex.org/W3123744736","https://openalex.org/W2137691148","https://openalex.org/W1505848319","https://openalex.org/W2127243424","https://openalex.org/W4390394189","https://openalex.org/W2905020035","https://openalex.org/W2037504162","https://openalex.org/W2539013788","https://openalex.org/W4281399881"],"abstract_inverted_index":{"We":[0,96],"present":[1],"a":[2,63,77,89],"matrix-factorization":[3,94],"algorithm":[4,30,43],"that":[5],"scales":[6],"to":[7,50,76,87,134],"input":[8],"matrices":[9],"with":[10,84],"both":[11,119],"huge":[12],"number":[13],"of":[14,62,92],"rows":[15,126],"and":[16,38,109,127],"columns.":[17],"Learned":[18],"factors":[19],"may":[20],"be":[21],"sparse":[22,35],"or":[23],"dense":[24],"and/or":[25],"nonnegative,":[26],"which":[27,121],"makes":[28],"our":[29],"suitable":[31],"for":[32],"dictionary":[33],"learning,":[34],"component":[36],"analysis,":[37],"nonnegative":[39],"matrix":[40,45,54],"factorization.":[41],"Our":[42,81],"streams":[44],"columns":[46],"while":[47],"subsampling":[48],"them":[49],"iteratively":[51],"learn":[52],"the":[53,59,93],"factors.":[55],"At":[56],"each":[57],"iteration,":[58],"row":[60],"dimension":[61],"new":[64],"sample":[65],"is":[66],"reduced":[67],"by":[68],"subsampling,":[69],"resulting":[70],"in":[71],"lower":[72],"time":[73],"complexity":[74],"compared":[75,133],"simple":[78],"streaming":[79],"algorithm.":[80],"method":[82],"comes":[83],"convergence":[85],"guarantees":[86],"reach":[88],"stationary":[90],"point":[91],"problem.":[95],"demonstrate":[97],"its":[98],"efficiency":[99],"on":[100,110,125],"massive":[101],"functional":[102],"magnetic":[103],"resonance":[104],"imaging":[105],"data":[106],"(2":[107],"TB),":[108],"patches":[111],"extracted":[112],"from":[113],"hyperspectral":[114],"images":[115],"(103":[116],"GB).":[117],"For":[118],"problems,":[120],"involve":[122],"different":[123],"penalties":[124],"columns,":[128],"we":[129],"obtain":[130],"significant":[131],"speed-ups":[132],"state-of-the-art":[135],"algorithms.":[136]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":2},{"year":2021,"cited_by_count":5},{"year":2020,"cited_by_count":10},{"year":2019,"cited_by_count":5},{"year":2018,"cited_by_count":4}],"updated_date":"2026-10-14T16:39:01.820549","created_date":"2025-10-10T00:00:00"}