{"id":"https://openalex.org/W2167167503","doi":"https://doi.org/10.1109/tsp.2015.2417491","title":"Subspace Learning and Imputation for Streaming Big Data Matrices and Tensors","display_name":"Subspace Learning and Imputation for Streaming Big Data Matrices and Tensors","publication_year":2015,"publication_date":"2015-03-31","ids":{"openalex":"https://openalex.org/W2167167503","doi":"https://doi.org/10.1109/tsp.2015.2417491","mag":"2167167503"},"language":"en","primary_location":{"id":"doi:10.1109/tsp.2015.2417491","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tsp.2015.2417491","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,"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"},"type":"article","indexed_in":["arxiv","crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/1404.4667","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5060755739","display_name":"Morteza Mardani","orcid":"https://orcid.org/0000-0002-3788-137X"},"institutions":[{"id":"https://openalex.org/I130238516","display_name":"University of Minnesota","ror":"https://ror.org/017zqws13","country_code":"US","type":"education","lineage":["https://openalex.org/I130238516"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Morteza Mardani","raw_affiliation_strings":["Department of Electrical and Computer Engineering, University of Minnesota, Minneapolis, MN"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Electrical and Computer Engineering, University of Minnesota, Minneapolis, MN","institution_ids":["https://openalex.org/I130238516"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5006078163","display_name":"Gonzalo Mateos","orcid":"https://orcid.org/0000-0002-9847-6298"},"institutions":[{"id":"https://openalex.org/I5388228","display_name":"University of Rochester","ror":"https://ror.org/022kthw22","country_code":"US","type":"education","lineage":["https://openalex.org/I5388228"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Gonzalo Mateos","raw_affiliation_strings":["Department of Electrical and Computer Engineering, University of Rochester, Rochester, NY"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Electrical and Computer Engineering, University of Rochester, Rochester, NY","institution_ids":["https://openalex.org/I5388228"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5026758314","display_name":"Georgios B. Giannakis","orcid":"https://orcid.org/0000-0002-0196-0260"},"institutions":[{"id":"https://openalex.org/I130238516","display_name":"University of Minnesota","ror":"https://ror.org/017zqws13","country_code":"US","type":"education","lineage":["https://openalex.org/I130238516"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Georgios B. Giannakis","raw_affiliation_strings":["Department of Electrical and Computer Engineering, University of Minnesota, Minneapolis, MN"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Electrical and Computer Engineering, University of Minnesota, Minneapolis, MN","institution_ids":["https://openalex.org/I130238516"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":28.4435,"has_fulltext":false,"cited_by_count":197,"citation_normalized_percentile":{"value":0.99891757,"is_in_top_1_percent":true,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":97,"max":100},"biblio":{"volume":"63","issue":"10","first_page":"2663","last_page":"2677"},"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/T12303","display_name":"Tensor decomposition and applications","score":0.9990000128746033,"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"}},{"id":"https://openalex.org/T10931","display_name":"Direction-of-Arrival Estimation Techniques","score":0.9908999800682068,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6896079778671265},{"id":"https://openalex.org/keywords/imputation","display_name":"Imputation (statistics)","score":0.6670932173728943},{"id":"https://openalex.org/keywords/missing-data","display_name":"Missing data","score":0.638989269733429},{"id":"https://openalex.org/keywords/subspace-topology","display_name":"Subspace topology","score":0.6279915571212769},{"id":"https://openalex.org/keywords/matrix-norm","display_name":"Matrix norm","score":0.5899083018302917},{"id":"https://openalex.org/keywords/big-data","display_name":"Big data","score":0.5138564109802246},{"id":"https://openalex.org/keywords/estimator","display_name":"Estimator","score":0.4955090284347534},{"id":"https://openalex.org/keywords/synthetic-data","display_name":"Synthetic data","score":0.4891565144062042},{"id":"https://openalex.org/keywords/matrix-completion","display_name":"Matrix completion","score":0.4571729302406311},{"id":"https://openalex.org/keywords/scalability","display_name":"Scalability","score":0.4459470510482788},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.4392213523387909},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.43247389793395996},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.3956252634525299},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.38251349329948425},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.33630573749542236},{"id":"https://openalex.org/keywords/gaussian","display_name":"Gaussian","score":0.2814609706401825},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.20368865132331848},{"id":"https://openalex.org/keywords/eigenvalues-and-eigenvectors","display_name":"Eigenvalues and eigenvectors","score":0.12050619721412659},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.1048017144203186}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6896079778671265},{"id":"https://openalex.org/C58041806","wikidata":"https://www.wikidata.org/wiki/Q1660484","display_name":"Imputation (statistics)","level":3,"score":0.6670932173728943},{"id":"https://openalex.org/C9357733","wikidata":"https://www.wikidata.org/wiki/Q6878417","display_name":"Missing data","level":2,"score":0.638989269733429},{"id":"https://openalex.org/C32834561","wikidata":"https://www.wikidata.org/wiki/Q660730","display_name":"Subspace topology","level":2,"score":0.6279915571212769},{"id":"https://openalex.org/C92207270","wikidata":"https://www.wikidata.org/wiki/Q939253","display_name":"Matrix norm","level":3,"score":0.5899083018302917},{"id":"https://openalex.org/C75684735","wikidata":"https://www.wikidata.org/wiki/Q858810","display_name":"Big data","level":2,"score":0.5138564109802246},{"id":"https://openalex.org/C185429906","wikidata":"https://www.wikidata.org/wiki/Q1130160","display_name":"Estimator","level":2,"score":0.4955090284347534},{"id":"https://openalex.org/C160920958","wikidata":"https://www.wikidata.org/wiki/Q7662746","display_name":"Synthetic data","level":2,"score":0.4891565144062042},{"id":"https://openalex.org/C2778459887","wikidata":"https://www.wikidata.org/wiki/Q6787865","display_name":"Matrix completion","level":3,"score":0.4571729302406311},{"id":"https://openalex.org/C48044578","wikidata":"https://www.wikidata.org/wiki/Q727490","display_name":"Scalability","level":2,"score":0.4459470510482788},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.4392213523387909},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.43247389793395996},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3956252634525299},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.38251349329948425},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.33630573749542236},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.2814609706401825},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.20368865132331848},{"id":"https://openalex.org/C158693339","wikidata":"https://www.wikidata.org/wiki/Q190524","display_name":"Eigenvalues and eigenvectors","level":2,"score":0.12050619721412659},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.1048017144203186},{"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/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0}],"mesh":[],"locations_count":4,"locations":[{"id":"doi:10.1109/tsp.2015.2417491","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tsp.2015.2417491","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,"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:1404.4667","is_oa":true,"landing_page_url":"http://arxiv.org/abs/1404.4667","pdf_url":"https://arxiv.org/pdf/1404.4667","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"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:CiteSeerX.psu:10.1.1.702.5418","is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.702.5418","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://www.ece.rochester.edu/%7Egmateosb/pubs/tensor/tensor_online_TSP.pdf","raw_type":"text"},{"id":"pmh:oai:CiteSeerX.psu:10.1.1.751.1730","is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.751.1730","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://arxiv.org/pdf/1404.4667.pdf","raw_type":"text"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:1404.4667","is_oa":true,"landing_page_url":"http://arxiv.org/abs/1404.4667","pdf_url":"https://arxiv.org/pdf/1404.4667","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"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":[],"awards":[{"id":"https://openalex.org/G4427125774","display_name":null,"funder_award_id":"AFOSR FA9550-10-1-0567","funder_id":"https://openalex.org/F4320333591","funder_display_name":"Multidisciplinary University Research Initiative"}],"funders":[{"id":"https://openalex.org/F4320333591","display_name":"Multidisciplinary University Research Initiative","ror":null}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":51,"referenced_works":["https://openalex.org/W1480376833","https://openalex.org/W1492459858","https://openalex.org/W1550574544","https://openalex.org/W1684305122","https://openalex.org/W1814521481","https://openalex.org/W1987142714","https://openalex.org/W1997299558","https://openalex.org/W2024165284","https://openalex.org/W2037360998","https://openalex.org/W2047071281","https://openalex.org/W2050770124","https://openalex.org/W2051245758","https://openalex.org/W2056405837","https://openalex.org/W2057503509","https://openalex.org/W2064894286","https://openalex.org/W2075406189","https://openalex.org/W2078677240","https://openalex.org/W2082600181","https://openalex.org/W2090698488","https://openalex.org/W2091449379","https://openalex.org/W2098171862","https://openalex.org/W2100556411","https://openalex.org/W2103284121","https://openalex.org/W2112447569","https://openalex.org/W2118080949","https://openalex.org/W2118550318","https://openalex.org/W2122090912","https://openalex.org/W2131389289","https://openalex.org/W2132743051","https://openalex.org/W2133515443","https://openalex.org/W2138505091","https://openalex.org/W2140738143","https://openalex.org/W2146850452","https://openalex.org/W2150226602","https://openalex.org/W2154992274","https://openalex.org/W2167755370","https://openalex.org/W2171250816","https://openalex.org/W2399506744","https://openalex.org/W2486096428","https://openalex.org/W2611328865","https://openalex.org/W2787894218","https://openalex.org/W2936995161","https://openalex.org/W2962834831","https://openalex.org/W2963937718","https://openalex.org/W4242209908","https://openalex.org/W6676727762","https://openalex.org/W6677671969","https://openalex.org/W6677826147","https://openalex.org/W6682001147","https://openalex.org/W6682766550","https://openalex.org/W6712781682"],"related_works":["https://openalex.org/W2181530120","https://openalex.org/W4211215373","https://openalex.org/W2024529227","https://openalex.org/W2055961818","https://openalex.org/W1574575415","https://openalex.org/W3144172081","https://openalex.org/W3179858851","https://openalex.org/W3028371478","https://openalex.org/W2081476516","https://openalex.org/W2581984549"],"abstract_inverted_index":{"Extracting":[0],"latent":[1,67],"low-dimensional":[2,63],"structure":[3,70],"from":[4,52,71],"high-dimensional":[5],"data":[6,188],"is":[7,82,119,154],"of":[8,35,58,138,160,192],"paramount":[9],"importance":[10],"in":[11],"timely":[12],"inference":[13],"tasks":[14],"encountered":[15],"with":[16,92,111,163,174],"\u201cBig":[17],"Data\u201d":[18],"analytics.":[19],"However,":[20],"increasingly":[21],"noisy,":[22],"heterogeneous,":[23],"and":[24,65,116,166,182,196],"incomplete":[25,72],"datasets,":[26],"as":[27,29,169,177,179],"well":[28,178],"the":[30,47,93,98,129,134,139,145,190,193],"need":[31],"for":[32],"real-time":[33,109],"processing":[34],"streaming":[36,73],"data,":[37,60,78],"pose":[38],"major":[39],"challenges":[40],"to":[41,55,106,156,201],"this":[42,45],"end.":[43],"In":[44,125],"context,":[46],"present":[48],"paper":[49],"permeates":[50],"benefits":[51],"rank":[53],"minimization":[54],"scalable":[56],"imputation":[57,168],"missing":[59,164],"via":[61],"tracking":[62],"subspaces":[64],"unraveling":[66],"(possibly":[68],"multi-way)":[69],"data.":[74],"For":[75],"low-rank":[76,161],"matrix":[77],"a":[79,103,126,149,170],"subspace":[80],"estimator":[81],"proposed":[83,194],"based":[84],"on":[85],"an":[86],"exponentially":[87],"weighted":[88],"least-squares":[89],"criterion":[90],"regularized":[91,142],"nuclear":[94,100],"norm.":[95],"After":[96],"recasting":[97],"nonseparable":[99],"norm":[101],"into":[102],"form":[104],"amenable":[105],"online":[107,151],"optimization,":[108],"algorithms":[110],"complementary":[112],"strengths":[113],"are":[114],"developed,":[115],"their":[117,197],"convergence":[118],"established":[120],"under":[121],"simplifying":[122],"technical":[123],"assumptions.":[124],"stationary":[127],"setting,":[128],"asymptotic":[130],"estimates":[131],"obtained":[132],"offer":[133],"well-documented":[135],"performance":[136,199],"guarantees":[137],"batch":[140],"nuclear-norm":[141],"estimator.":[143],"Under":[144],"same":[146],"unifying":[147],"framework,":[148],"novel":[150],"(adaptive)":[152],"algorithm":[153],"developed":[155],"obtain":[157],"multi-way":[158],"decompositions":[159],"tensors":[162],"entries":[165],"perform":[167],"byproduct.":[171],"Simulated":[172],"tests":[173],"both":[175],"synthetic":[176],"real":[180],"Internet":[181],"cardiac":[183],"magnetic":[184],"resonance":[185],"imagery":[186],"(MRI)":[187],"confirm":[189],"efficacy":[191],"algorithms,":[195],"superior":[198],"relative":[200],"state-of-the-art":[202],"alternatives.":[203]},"counts_by_year":[{"year":2026,"cited_by_count":2},{"year":2025,"cited_by_count":5},{"year":2024,"cited_by_count":9},{"year":2023,"cited_by_count":13},{"year":2022,"cited_by_count":25},{"year":2021,"cited_by_count":21},{"year":2020,"cited_by_count":14},{"year":2019,"cited_by_count":16},{"year":2018,"cited_by_count":23},{"year":2017,"cited_by_count":22},{"year":2016,"cited_by_count":29},{"year":2015,"cited_by_count":14},{"year":2014,"cited_by_count":4}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}