{"id":"https://openalex.org/W4363650386","doi":"https://doi.org/10.1080/00401706.2023.2200541","title":"Robust Low-Rank Tensor Decomposition with the L <sub>2</sub> Criterion","display_name":"Robust Low-Rank Tensor Decomposition with the L <sub>2</sub> Criterion","publication_year":2023,"publication_date":"2023-04-10","ids":{"openalex":"https://openalex.org/W4363650386","doi":"https://doi.org/10.1080/00401706.2023.2200541","pmid":"https://pubmed.ncbi.nlm.nih.gov/38213317"},"language":"en","primary_location":{"id":"doi:10.1080/00401706.2023.2200541","is_oa":false,"landing_page_url":"https://doi.org/10.1080/00401706.2023.2200541","pdf_url":null,"source":{"id":"https://openalex.org/S985303","display_name":"Technometrics","issn_l":"0040-1706","issn":["0040-1706","1537-2723"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320547","host_organization_name":"Taylor & Francis","host_organization_lineage":["https://openalex.org/P4310320547"],"host_organization_lineage_names":["Taylor & Francis"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Technometrics","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","pubmed"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://pmc.ncbi.nlm.nih.gov/articles/PMC10783176/pdf/nihms-1915074.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5091358424","display_name":"Qiang Heng","orcid":"https://orcid.org/0000-0002-4042-6773"},"institutions":[{"id":"https://openalex.org/I137902535","display_name":"North Carolina State University","ror":"https://ror.org/04tj63d06","country_code":"US","type":"education","lineage":["https://openalex.org/I137902535"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Qiang Heng","raw_affiliation_strings":["Department of Statistics, North Carolina State University, Raleigh, NC"],"raw_orcid":"https://orcid.org/0000-0002-4042-6773","affiliations":[{"raw_affiliation_string":"Department of Statistics, North Carolina State University, Raleigh, NC","institution_ids":["https://openalex.org/I137902535"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5089636420","display_name":"C. Eric","orcid":"https://orcid.org/0000-0003-4647-0895"},"institutions":[{"id":"https://openalex.org/I74775410","display_name":"Rice University","ror":"https://ror.org/008zs3103","country_code":"US","type":"education","lineage":["https://openalex.org/I74775410"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Eric C. Chi","raw_affiliation_strings":["Department of Statistics, Rice University, Houston, TX"],"raw_orcid":"https://orcid.org/0000-0003-4647-0895","affiliations":[{"raw_affiliation_string":"Department of Statistics, Rice University, Houston, TX","institution_ids":["https://openalex.org/I74775410"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100376614","display_name":"Yufeng Liu","orcid":"https://orcid.org/0000-0002-1686-0545"},"institutions":[{"id":"https://openalex.org/I114027177","display_name":"University of North Carolina at Chapel Hill","ror":"https://ror.org/0130frc33","country_code":"US","type":"education","lineage":["https://openalex.org/I114027177"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Yufeng Liu","raw_affiliation_strings":["Department of Statistics and Operations Research, Department of Genetics, Department of Biostatistics, The University of North Carolina at Chapel Hill, Chapel Hill, NC"],"raw_orcid":"https://orcid.org/0000-0002-1686-0545","affiliations":[{"raw_affiliation_string":"Department of Statistics and Operations Research, Department of Genetics, Department of Biostatistics, The University of North Carolina at Chapel Hill, Chapel Hill, NC","institution_ids":["https://openalex.org/I114027177"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5089636420"],"corresponding_institution_ids":["https://openalex.org/I74775410"],"apc_list":null,"apc_paid":null,"fwci":0.9572,"has_fulltext":true,"cited_by_count":5,"citation_normalized_percentile":{"value":0.69491525,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":94,"max":97},"biblio":{"volume":"65","issue":"4","first_page":"537","last_page":"552"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12303","display_name":"Tensor decomposition and applications","score":0.9998000264167786,"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"}},"topics":[{"id":"https://openalex.org/T12303","display_name":"Tensor decomposition and applications","score":0.9998000264167786,"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/T10500","display_name":"Sparse and Compressive Sensing Techniques","score":0.9872000217437744,"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/T11304","display_name":"Advanced Neuroimaging Techniques and Applications","score":0.9228000044822693,"subfield":{"id":"https://openalex.org/subfields/2741","display_name":"Radiology, Nuclear Medicine and Imaging"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/tucker-decomposition","display_name":"Tucker decomposition","score":0.9017006158828735},{"id":"https://openalex.org/keywords/outlier","display_name":"Outlier","score":0.8248904943466187},{"id":"https://openalex.org/keywords/tensor","display_name":"Tensor (intrinsic definition)","score":0.7492982745170593},{"id":"https://openalex.org/keywords/rank","display_name":"Rank (graph theory)","score":0.7272828817367554},{"id":"https://openalex.org/keywords/estimator","display_name":"Estimator","score":0.6309882998466492},{"id":"https://openalex.org/keywords/decomposition","display_name":"Decomposition","score":0.5762594938278198},{"id":"https://openalex.org/keywords/tensor-decomposition","display_name":"Tensor decomposition","score":0.5759614109992981},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5468733310699463},{"id":"https://openalex.org/keywords/robust-statistics","display_name":"Robust statistics","score":0.4965136647224426},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4827003479003906},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.4593205451965332},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.44106563925743103},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.43909355998039246},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.42236828804016113},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.2972073554992676},{"id":"https://openalex.org/keywords/combinatorics","display_name":"Combinatorics","score":0.09516805410385132}],"concepts":[{"id":"https://openalex.org/C42704193","wikidata":"https://www.wikidata.org/wiki/Q7851097","display_name":"Tucker decomposition","level":4,"score":0.9017006158828735},{"id":"https://openalex.org/C79337645","wikidata":"https://www.wikidata.org/wiki/Q779824","display_name":"Outlier","level":2,"score":0.8248904943466187},{"id":"https://openalex.org/C155281189","wikidata":"https://www.wikidata.org/wiki/Q3518150","display_name":"Tensor (intrinsic definition)","level":2,"score":0.7492982745170593},{"id":"https://openalex.org/C164226766","wikidata":"https://www.wikidata.org/wiki/Q7293202","display_name":"Rank (graph theory)","level":2,"score":0.7272828817367554},{"id":"https://openalex.org/C185429906","wikidata":"https://www.wikidata.org/wiki/Q1130160","display_name":"Estimator","level":2,"score":0.6309882998466492},{"id":"https://openalex.org/C124681953","wikidata":"https://www.wikidata.org/wiki/Q339062","display_name":"Decomposition","level":2,"score":0.5762594938278198},{"id":"https://openalex.org/C2986737658","wikidata":"https://www.wikidata.org/wiki/Q30103009","display_name":"Tensor decomposition","level":3,"score":0.5759614109992981},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5468733310699463},{"id":"https://openalex.org/C67226441","wikidata":"https://www.wikidata.org/wiki/Q1665389","display_name":"Robust statistics","level":3,"score":0.4965136647224426},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4827003479003906},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.4593205451965332},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.44106563925743103},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.43909355998039246},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.42236828804016113},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.2972073554992676},{"id":"https://openalex.org/C114614502","wikidata":"https://www.wikidata.org/wiki/Q76592","display_name":"Combinatorics","level":1,"score":0.09516805410385132},{"id":"https://openalex.org/C104317684","wikidata":"https://www.wikidata.org/wiki/Q7187","display_name":"Gene","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},{"id":"https://openalex.org/C18903297","wikidata":"https://www.wikidata.org/wiki/Q7150","display_name":"Ecology","level":1,"score":0.0},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C55493867","wikidata":"https://www.wikidata.org/wiki/Q7094","display_name":"Biochemistry","level":1,"score":0.0},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.1080/00401706.2023.2200541","is_oa":false,"landing_page_url":"https://doi.org/10.1080/00401706.2023.2200541","pdf_url":null,"source":{"id":"https://openalex.org/S985303","display_name":"Technometrics","issn_l":"0040-1706","issn":["0040-1706","1537-2723"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320547","host_organization_name":"Taylor & Francis","host_organization_lineage":["https://openalex.org/P4310320547"],"host_organization_lineage_names":["Taylor & Francis"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Technometrics","raw_type":"journal-article"},{"id":"pmid:38213317","is_oa":false,"landing_page_url":"https://pubmed.ncbi.nlm.nih.gov/38213317","pdf_url":null,"source":{"id":"https://openalex.org/S4306525036","display_name":"PubMed","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1299303238","host_organization_name":"National Institutes of Health","host_organization_lineage":["https://openalex.org/I1299303238"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Technometrics : a journal of statistics for the physical, chemical, and engineering sciences","raw_type":null},{"id":"pmh:oai:pubmedcentral.nih.gov:10783176","is_oa":true,"landing_page_url":"https://www.ncbi.nlm.nih.gov/pmc/articles/10783176","pdf_url":"https://pmc.ncbi.nlm.nih.gov/articles/PMC10783176/pdf/nihms-1915074.pdf","source":{"id":"https://openalex.org/S2764455111","display_name":"PubMed Central","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1299303238","host_organization_name":"National Institutes of Health","host_organization_lineage":["https://openalex.org/I1299303238"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Technometrics","raw_type":"Text"}],"best_oa_location":{"id":"pmh:oai:pubmedcentral.nih.gov:10783176","is_oa":true,"landing_page_url":"https://www.ncbi.nlm.nih.gov/pmc/articles/10783176","pdf_url":"https://pmc.ncbi.nlm.nih.gov/articles/PMC10783176/pdf/nihms-1915074.pdf","source":{"id":"https://openalex.org/S2764455111","display_name":"PubMed Central","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1299303238","host_organization_name":"National Institutes of Health","host_organization_lineage":["https://openalex.org/I1299303238"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Technometrics","raw_type":"Text"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G1209886030","display_name":null,"funder_award_id":"R01 GM126550","funder_id":"https://openalex.org/F4320337354","funder_display_name":"National Institute of General Medical Sciences"},{"id":"https://openalex.org/G4411993078","display_name":"CAREER: Stable and Scalable Estimation of the Intrinsic Geometry of Multiway Data","funder_award_id":"2201136","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G5022066200","display_name":null,"funder_award_id":"R01 GM135928","funder_id":"https://openalex.org/F4320337354","funder_display_name":"National Institute of General Medical Sciences"},{"id":"https://openalex.org/G6001831890","display_name":null,"funder_award_id":"R01GM135928","funder_id":"https://openalex.org/F4320337354","funder_display_name":"National Institute of General Medical Sciences"},{"id":"https://openalex.org/G6292322299","display_name":null,"funder_award_id":"R01GM135928: EC","funder_id":"https://openalex.org/F4320337354","funder_display_name":"National Institute of General Medical Sciences"},{"id":"https://openalex.org/G6298394821","display_name":null,"funder_award_id":"DMS-2201136: EC, DMS-2100729: YL","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G7248835867","display_name":null,"funder_award_id":"R01GM126550","funder_id":"https://openalex.org/F4320337354","funder_display_name":"National Institute of General Medical Sciences"},{"id":"https://openalex.org/G8828146094","display_name":null,"funder_award_id":"R01GM126550: YL","funder_id":"https://openalex.org/F4320337354","funder_display_name":"National Institute of General Medical Sciences"}],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"},{"id":"https://openalex.org/F4320332161","display_name":"National Institutes of Health","ror":"https://ror.org/01cwqze88"},{"id":"https://openalex.org/F4320337354","display_name":"National Institute of General Medical Sciences","ror":"https://ror.org/04q48ey07"}],"has_content":{"pdf":true,"grobid_xml":false},"content_urls":{"pdf":"https://content.openalex.org/works/W4363650386.pdf"},"referenced_works_count":66,"referenced_works":["https://openalex.org/W792141054","https://openalex.org/W1529918996","https://openalex.org/W1788130325","https://openalex.org/W1814521481","https://openalex.org/W1963826206","https://openalex.org/W1968956560","https://openalex.org/W1983467829","https://openalex.org/W1991042426","https://openalex.org/W1993482030","https://openalex.org/W1999136078","https://openalex.org/W2000215628","https://openalex.org/W2000359198","https://openalex.org/W2005126631","https://openalex.org/W2007339694","https://openalex.org/W2009609465","https://openalex.org/W2013912476","https://openalex.org/W2018282388","https://openalex.org/W2023199413","https://openalex.org/W2024165284","https://openalex.org/W2032320656","https://openalex.org/W2033560557","https://openalex.org/W2035399158","https://openalex.org/W2037271374","https://openalex.org/W2041490670","https://openalex.org/W2043571470","https://openalex.org/W2051434435","https://openalex.org/W2071729267","https://openalex.org/W2078677240","https://openalex.org/W2084017011","https://openalex.org/W2096575354","https://openalex.org/W2105974898","https://openalex.org/W2108138101","https://openalex.org/W2118080949","https://openalex.org/W2137089267","https://openalex.org/W2145962650","https://openalex.org/W2157382843","https://openalex.org/W2157656099","https://openalex.org/W2405968727","https://openalex.org/W2428204121","https://openalex.org/W2431890537","https://openalex.org/W2520465971","https://openalex.org/W2547812180","https://openalex.org/W2593198395","https://openalex.org/W2597667148","https://openalex.org/W2624172411","https://openalex.org/W2726993892","https://openalex.org/W2772124661","https://openalex.org/W2963885538","https://openalex.org/W2964121447","https://openalex.org/W2995423737","https://openalex.org/W3005990752","https://openalex.org/W3007235251","https://openalex.org/W3034900323","https://openalex.org/W3103400026","https://openalex.org/W3127320487","https://openalex.org/W3139260387","https://openalex.org/W3157992902","https://openalex.org/W3168377052","https://openalex.org/W4210625977","https://openalex.org/W4233352909","https://openalex.org/W4234552385","https://openalex.org/W4292363360","https://openalex.org/W4293116277","https://openalex.org/W6675725816","https://openalex.org/W6717575008","https://openalex.org/W6767581218"],"related_works":["https://openalex.org/W2891735857","https://openalex.org/W4214526161","https://openalex.org/W47805180","https://openalex.org/W2093953080","https://openalex.org/W2564982703","https://openalex.org/W3127610061","https://openalex.org/W2891277085","https://openalex.org/W2347172331","https://openalex.org/W3216281372","https://openalex.org/W4281643854"],"abstract_inverted_index":{"The":[0,63,78],"growing":[1],"prevalence":[2],"of":[3,81,95,103],"tensor":[4,18,91],"data,":[5,97],"or":[6,75],"multiway":[7],"arrays,":[8],"in":[9,54,69,89],"science":[10],"and":[11,98],"engineering":[12],"applications":[13,88],"motivates":[14],"the":[15,37,41],"need":[16],"for":[17,101],"decompositions":[19],"that":[20,47],"are":[21],"robust":[22,31],"against":[23],"outliers.":[24],"In":[25],"this":[26],"article,":[27],"we":[28],"present":[29],"a":[30,70],"Tucker":[32],"decomposition":[33],"estimator":[34],"based":[35],"on":[36,85],"L2":[38],"criterion,":[39],"called":[40],"Tucker-L2E.":[42],"Our":[43],"numerical":[44],"experiments":[45],"demonstrate":[46],"Tucker-L2E":[48,82],"has":[49],"empirically":[50],"stronger":[51],"recovery":[52],"performance":[53],"more":[55],"challenging":[56],"high-rank":[57],"scenarios":[58],"compared":[59],"with":[60,73],"existing":[61],"alternatives.":[62],"appropriate":[64],"Tucker-rank":[65],"can":[66],"be":[67],"selected":[68],"data-driven":[71],"manner":[72],"cross-validation":[74],"hold-out":[76],"validation.":[77],"practical":[79],"effectiveness":[80],"is":[83],"validated":[84],"real":[86],"data":[87],"fMRI":[90],"denoising,":[92],"PARAFAC":[93],"analysis":[94],"fluorescence":[96],"feature":[99],"extraction":[100],"classification":[102],"corrupted":[104],"images.":[105]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":2}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2023-04-11T00:00:00"}
