{"id":"https://openalex.org/W2289262545","doi":"https://doi.org/10.1109/apsipa.2015.7415348","title":"A fast automatic low-rank determination algorithm for noisy matrix completion","display_name":"A fast automatic low-rank determination algorithm for noisy matrix completion","publication_year":2015,"publication_date":"2015-12-01","ids":{"openalex":"https://openalex.org/W2289262545","doi":"https://doi.org/10.1109/apsipa.2015.7415348","mag":"2289262545"},"language":"en","primary_location":{"id":"doi:10.1109/apsipa.2015.7415348","is_oa":false,"landing_page_url":"https://doi.org/10.1109/apsipa.2015.7415348","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2015 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA)","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://nitech.repo.nii.ac.jp/records/6336","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5039764322","display_name":"Tatsuya Yokota","orcid":"https://orcid.org/0000-0002-7368-2060"},"institutions":[{"id":"https://openalex.org/I2800939219","display_name":"RIKEN Center for Brain Science","ror":"https://ror.org/04j1n1c04","country_code":"JP","type":"facility","lineage":["https://openalex.org/I2800939219","https://openalex.org/I4210110652"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Tatsuya Yokota","raw_affiliation_strings":["RIKEN Brain Science Institute, Japan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"RIKEN Brain Science Institute, Japan","institution_ids":["https://openalex.org/I2800939219"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5018676117","display_name":"Andrzej Cichocki","orcid":"https://orcid.org/0000-0002-8364-7226"},"institutions":[{"id":"https://openalex.org/I2800939219","display_name":"RIKEN Center for Brain Science","ror":"https://ror.org/04j1n1c04","country_code":"JP","type":"facility","lineage":["https://openalex.org/I2800939219","https://openalex.org/I4210110652"]},{"id":"https://openalex.org/I66083562","display_name":"Systems Research Institute","ror":"https://ror.org/0111cp837","country_code":"PL","type":"facility","lineage":["https://openalex.org/I66083562","https://openalex.org/I99542240"]}],"countries":["JP","PL"],"is_corresponding":false,"raw_author_name":"Andrzej Cichocki","raw_affiliation_strings":["Polish Academy of Science (PAN), System Research Institute, Poland","RIKEN Brain Science Institute, Japan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Polish Academy of Science (PAN), System Research Institute, Poland","institution_ids":["https://openalex.org/I66083562"]},{"raw_affiliation_string":"RIKEN Brain Science Institute, Japan","institution_ids":["https://openalex.org/I2800939219"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":1.7218,"has_fulltext":true,"cited_by_count":4,"citation_normalized_percentile":{"value":0.84107239,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":96},"biblio":{"volume":"46","issue":null,"first_page":"43","last_page":"46"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10500","display_name":"Sparse and Compressive Sensing Techniques","score":1.0,"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":1.0,"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/T11447","display_name":"Blind Source Separation Techniques","score":0.9990000128746033,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"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/T10688","display_name":"Image and Signal Denoising Methods","score":0.9965000152587891,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/matrix-norm","display_name":"Matrix norm","score":0.8364537358283997},{"id":"https://openalex.org/keywords/matrix-completion","display_name":"Matrix completion","score":0.7929558157920837},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.6361255645751953},{"id":"https://openalex.org/keywords/low-rank-approximation","display_name":"Low-rank approximation","score":0.612101137638092},{"id":"https://openalex.org/keywords/rank","display_name":"Rank (graph theory)","score":0.5992416739463806},{"id":"https://openalex.org/keywords/matrix","display_name":"Matrix (chemical analysis)","score":0.5316734313964844},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5129275321960449},{"id":"https://openalex.org/keywords/minification","display_name":"Minification","score":0.4532011151313782},{"id":"https://openalex.org/keywords/mathematical-optimization","display_name":"Mathematical optimization","score":0.4326784908771515},{"id":"https://openalex.org/keywords/regularization","display_name":"Regularization (linguistics)","score":0.424209326505661},{"id":"https://openalex.org/keywords/tensor","display_name":"Tensor (intrinsic definition)","score":0.3889543414115906},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.3298100233078003},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.19295960664749146},{"id":"https://openalex.org/keywords/combinatorics","display_name":"Combinatorics","score":0.08020591735839844}],"concepts":[{"id":"https://openalex.org/C92207270","wikidata":"https://www.wikidata.org/wiki/Q939253","display_name":"Matrix norm","level":3,"score":0.8364537358283997},{"id":"https://openalex.org/C2778459887","wikidata":"https://www.wikidata.org/wiki/Q6787865","display_name":"Matrix completion","level":3,"score":0.7929558157920837},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.6361255645751953},{"id":"https://openalex.org/C90199385","wikidata":"https://www.wikidata.org/wiki/Q6692777","display_name":"Low-rank approximation","level":3,"score":0.612101137638092},{"id":"https://openalex.org/C164226766","wikidata":"https://www.wikidata.org/wiki/Q7293202","display_name":"Rank (graph theory)","level":2,"score":0.5992416739463806},{"id":"https://openalex.org/C106487976","wikidata":"https://www.wikidata.org/wiki/Q685816","display_name":"Matrix (chemical analysis)","level":2,"score":0.5316734313964844},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5129275321960449},{"id":"https://openalex.org/C147764199","wikidata":"https://www.wikidata.org/wiki/Q6865248","display_name":"Minification","level":2,"score":0.4532011151313782},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.4326784908771515},{"id":"https://openalex.org/C2776135515","wikidata":"https://www.wikidata.org/wiki/Q17143721","display_name":"Regularization (linguistics)","level":2,"score":0.424209326505661},{"id":"https://openalex.org/C155281189","wikidata":"https://www.wikidata.org/wiki/Q3518150","display_name":"Tensor (intrinsic definition)","level":2,"score":0.3889543414115906},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.3298100233078003},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.19295960664749146},{"id":"https://openalex.org/C114614502","wikidata":"https://www.wikidata.org/wiki/Q76592","display_name":"Combinatorics","level":1,"score":0.08020591735839844},{"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/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/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"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/C158693339","wikidata":"https://www.wikidata.org/wiki/Q190524","display_name":"Eigenvalues and eigenvectors","level":2,"score":0.0},{"id":"https://openalex.org/C202444582","wikidata":"https://www.wikidata.org/wiki/Q837863","display_name":"Pure mathematics","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/apsipa.2015.7415348","is_oa":false,"landing_page_url":"https://doi.org/10.1109/apsipa.2015.7415348","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2015 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA)","raw_type":"proceedings-article"},{"id":"pmh:oai:irdb.nii.ac.jp:01154:0001438132","is_oa":true,"landing_page_url":"https://nitech.repo.nii.ac.jp/records/6336","pdf_url":null,"source":{"id":"https://openalex.org/S7407056385","display_name":"Institutional Repositories DataBase (IRDB)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I184597095","host_organization_name":"National Institute of Informatics","host_organization_lineage":["https://openalex.org/I184597095"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"2015 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA)","raw_type":"conference paper"}],"best_oa_location":{"id":"pmh:oai:irdb.nii.ac.jp:01154:0001438132","is_oa":true,"landing_page_url":"https://nitech.repo.nii.ac.jp/records/6336","pdf_url":null,"source":{"id":"https://openalex.org/S7407056385","display_name":"Institutional Repositories DataBase (IRDB)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I184597095","host_organization_name":"National Institute of Informatics","host_organization_lineage":["https://openalex.org/I184597095"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"2015 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA)","raw_type":"conference paper"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":21,"referenced_works":["https://openalex.org/W1246381107","https://openalex.org/W1548802052","https://openalex.org/W1736339626","https://openalex.org/W1798398164","https://openalex.org/W1928564215","https://openalex.org/W2000157792","https://openalex.org/W2000215628","https://openalex.org/W2011359124","https://openalex.org/W2014466433","https://openalex.org/W2047071281","https://openalex.org/W2098290597","https://openalex.org/W2103972604","https://openalex.org/W2118550318","https://openalex.org/W2144730813","https://openalex.org/W2154249783","https://openalex.org/W2184161947","https://openalex.org/W2611328865","https://openalex.org/W3203224907","https://openalex.org/W6640366772","https://openalex.org/W6929297771","https://openalex.org/W6929385289"],"related_works":["https://openalex.org/W2106005123","https://openalex.org/W2964006653","https://openalex.org/W2788826952","https://openalex.org/W4302315572","https://openalex.org/W2084983808","https://openalex.org/W1991825408","https://openalex.org/W4318564253","https://openalex.org/W1969698720","https://openalex.org/W2325477568","https://openalex.org/W2776849335"],"abstract_inverted_index":{"Rank":[0],"estimation":[1],"is":[2,41],"an":[3],"important":[4],"factor":[5],"for":[6,82,99,112],"low-rank":[7],"based":[8,80],"matrix":[9,27,46,61,101],"completion,":[10],"and":[11,63,85,103,106],"most":[12],"works":[13],"devoted":[14],"to":[15,35,38,43,58],"this":[16],"problem":[17],"have":[18],"considered":[19],"the":[20,74],"minimization":[21,33],"of":[22,26,115],"nuclear":[23,31,68],"norm":[24,32],"instead":[25],"rank.":[28],"However,":[29],"when":[30],"shifts":[34],"`regularization'":[36],"due":[37],"noise,":[39],"it":[40],"difficult":[42],"estimate":[44,60],"original":[45],"rank,":[47],"precisely.":[48],"In":[49,70],"present":[50],"paper,":[51],"we":[52],"propose":[53],"a":[54],"new":[55],"fast":[56],"algorithm":[57,76],"precisely":[59],"rank":[62],"perform":[64],"completion":[65],"without":[66],"using":[67],"norm.":[69],"our":[71],"extensive":[72],"experiments,":[73],"proposed":[75],"significantly":[77],"outperformed":[78],"nuclear-norm":[79],"method":[81],"accuracy,":[83],"especially":[84],"Incremental":[86],"OptSpace":[87],"regarding":[88],"computational":[89],"time.":[90],"Our":[91],"model":[92],"selection":[93],"scheme":[94],"has":[95],"many":[96],"promising":[97],"extensions":[98,108],"constrained":[100],"factorizations":[102],"tensor":[104],"decompositions,":[105],"these":[107],"could":[109],"be":[110],"useful":[111],"wide":[113],"range":[114],"practical":[116],"applications.":[117]},"counts_by_year":[{"year":2019,"cited_by_count":1},{"year":2018,"cited_by_count":2},{"year":2016,"cited_by_count":1}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}
