{"id":"https://openalex.org/W4294310650","doi":"https://doi.org/10.1109/tkde.2022.3203904","title":"Learning Linear and Nonlinear Low-Rank Structure in Multi-Task Learning","display_name":"Learning Linear and Nonlinear Low-Rank Structure in Multi-Task Learning","publication_year":2022,"publication_date":"2022-01-01","ids":{"openalex":"https://openalex.org/W4294310650","doi":"https://doi.org/10.1109/tkde.2022.3203904"},"language":"en","primary_location":{"id":"doi:10.1109/tkde.2022.3203904","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tkde.2022.3203904","pdf_url":null,"source":{"id":"https://openalex.org/S30698027","display_name":"IEEE Transactions on Knowledge and Data Engineering","issn_l":"1041-4347","issn":["1041-4347","1558-2191","2326-3865"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320439","host_organization_name":"IEEE Computer Society","host_organization_lineage":["https://openalex.org/P4310320439","https://openalex.org/P4310319808"],"host_organization_lineage_names":["IEEE Computer Society","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 Knowledge and Data Engineering","raw_type":"journal-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/A5100388333","display_name":"Yi Zhang","orcid":"https://orcid.org/0000-0003-4299-1511"},"institutions":[{"id":"https://openalex.org/I3045169105","display_name":"Southern University of Science and Technology","ror":"https://ror.org/049tv2d57","country_code":"CN","type":"education","lineage":["https://openalex.org/I3045169105"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Yi Zhang","raw_affiliation_strings":["Department of Computer Science and Engineering and Research Institute of Trustworthy Autonomous Systems, Southern University of Science and Technology, China","Guangdong Provincial Key Laboratory of Brain-inspired Intelligent Computation, Department of Computer Science and Engineering, Southern University of Science and Technology, China"],"affiliations":[{"raw_affiliation_string":"Department of Computer Science and Engineering and Research Institute of Trustworthy Autonomous Systems, Southern University of Science and Technology, China","institution_ids":["https://openalex.org/I3045169105"]},{"raw_affiliation_string":"Guangdong Provincial Key Laboratory of Brain-inspired Intelligent Computation, Department of Computer Science and Engineering, Southern University of Science and Technology, China","institution_ids":["https://openalex.org/I3045169105"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100433691","display_name":"Yu Zhang","orcid":"https://orcid.org/0000-0003-1100-4835"},"institutions":[{"id":"https://openalex.org/I3045169105","display_name":"Southern University of Science and Technology","ror":"https://ror.org/049tv2d57","country_code":"CN","type":"education","lineage":["https://openalex.org/I3045169105"]},{"id":"https://openalex.org/I4210136793","display_name":"Peng Cheng Laboratory","ror":"https://ror.org/03qdqbt06","country_code":"CN","type":"facility","lineage":["https://openalex.org/I4210136793"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yu Zhang","raw_affiliation_strings":["Department of Computer Science and Engineering and Research Institute of Trustworthy Autonomous Systems, Southern University of Science and Technology, China","Guangdong Provincial Key Laboratory of Brain-inspired Intelligent Computation, Department of Computer Science and Engineering, Southern University of Science and Technology, China","Peng Cheng Laboratory, China"],"affiliations":[{"raw_affiliation_string":"Department of Computer Science and Engineering and Research Institute of Trustworthy Autonomous Systems, Southern University of Science and Technology, China","institution_ids":["https://openalex.org/I3045169105"]},{"raw_affiliation_string":"Guangdong Provincial Key Laboratory of Brain-inspired Intelligent Computation, Department of Computer Science and Engineering, Southern University of Science and Technology, China","institution_ids":["https://openalex.org/I3045169105"]},{"raw_affiliation_string":"Peng Cheng Laboratory, China","institution_ids":["https://openalex.org/I4210136793"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100392060","display_name":"Wei Wang","orcid":"https://orcid.org/0000-0002-7080-3381"},"institutions":[{"id":"https://openalex.org/I881766915","display_name":"Nanjing University","ror":"https://ror.org/01rxvg760","country_code":"CN","type":"education","lineage":["https://openalex.org/I881766915"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Wei Wang","raw_affiliation_strings":["National Key Laboratory for Novel Software Technology, Nanjing University, China"],"affiliations":[{"raw_affiliation_string":"National Key Laboratory for Novel Software Technology, Nanjing University, China","institution_ids":["https://openalex.org/I881766915"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5100388333"],"corresponding_institution_ids":["https://openalex.org/I3045169105"],"apc_list":null,"apc_paid":null,"fwci":0.6687,"has_fulltext":false,"cited_by_count":5,"citation_normalized_percentile":{"value":0.65805658,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":94,"max":97},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"12"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12303","display_name":"Tensor decomposition and applications","score":0.9998999834060669,"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":0.9998999834060669,"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":0.9926999807357788,"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/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.9399999976158142,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6949772834777832},{"id":"https://openalex.org/keywords/trace","display_name":"TRACE (psycholinguistics)","score":0.6552841067314148},{"id":"https://openalex.org/keywords/rank","display_name":"Rank (graph theory)","score":0.6166955232620239},{"id":"https://openalex.org/keywords/tensor","display_name":"Tensor (intrinsic definition)","score":0.6033816933631897},{"id":"https://openalex.org/keywords/matrix-norm","display_name":"Matrix norm","score":0.5679546594619751},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.4771396815776825},{"id":"https://openalex.org/keywords/norm","display_name":"Norm (philosophy)","score":0.47647354006767273},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.42757970094680786},{"id":"https://openalex.org/keywords/nonlinear-system","display_name":"Nonlinear system","score":0.4268991947174072},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.38121843338012695},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3647378087043762},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.20985731482505798},{"id":"https://openalex.org/keywords/pure-mathematics","display_name":"Pure mathematics","score":0.06958937644958496}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6949772834777832},{"id":"https://openalex.org/C75291252","wikidata":"https://www.wikidata.org/wiki/Q1315756","display_name":"TRACE (psycholinguistics)","level":2,"score":0.6552841067314148},{"id":"https://openalex.org/C164226766","wikidata":"https://www.wikidata.org/wiki/Q7293202","display_name":"Rank (graph theory)","level":2,"score":0.6166955232620239},{"id":"https://openalex.org/C155281189","wikidata":"https://www.wikidata.org/wiki/Q3518150","display_name":"Tensor (intrinsic definition)","level":2,"score":0.6033816933631897},{"id":"https://openalex.org/C92207270","wikidata":"https://www.wikidata.org/wiki/Q939253","display_name":"Matrix norm","level":3,"score":0.5679546594619751},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.4771396815776825},{"id":"https://openalex.org/C191795146","wikidata":"https://www.wikidata.org/wiki/Q3878446","display_name":"Norm (philosophy)","level":2,"score":0.47647354006767273},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.42757970094680786},{"id":"https://openalex.org/C158622935","wikidata":"https://www.wikidata.org/wiki/Q660848","display_name":"Nonlinear system","level":2,"score":0.4268991947174072},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.38121843338012695},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3647378087043762},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.20985731482505798},{"id":"https://openalex.org/C202444582","wikidata":"https://www.wikidata.org/wiki/Q837863","display_name":"Pure mathematics","level":1,"score":0.06958937644958496},{"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/C17744445","wikidata":"https://www.wikidata.org/wiki/Q36442","display_name":"Political science","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/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","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/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C114614502","wikidata":"https://www.wikidata.org/wiki/Q76592","display_name":"Combinatorics","level":1,"score":0.0},{"id":"https://openalex.org/C13280743","wikidata":"https://www.wikidata.org/wiki/Q131089","display_name":"Geodesy","level":1,"score":0.0},{"id":"https://openalex.org/C199539241","wikidata":"https://www.wikidata.org/wiki/Q7748","display_name":"Law","level":1,"score":0.0},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tkde.2022.3203904","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tkde.2022.3203904","pdf_url":null,"source":{"id":"https://openalex.org/S30698027","display_name":"IEEE Transactions on Knowledge and Data Engineering","issn_l":"1041-4347","issn":["1041-4347","1558-2191","2326-3865"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320439","host_organization_name":"IEEE Computer Society","host_organization_lineage":["https://openalex.org/P4310320439","https://openalex.org/P4310319808"],"host_organization_lineage_names":["IEEE Computer Society","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 Knowledge and Data Engineering","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":60,"referenced_works":["https://openalex.org/W1520871589","https://openalex.org/W1522301498","https://openalex.org/W1686810756","https://openalex.org/W1942758450","https://openalex.org/W1963826206","https://openalex.org/W1993482030","https://openalex.org/W1999140058","https://openalex.org/W2029213856","https://openalex.org/W2040649920","https://openalex.org/W2045704273","https://openalex.org/W2091449379","https://openalex.org/W2111399684","https://openalex.org/W2112093930","https://openalex.org/W2119595900","https://openalex.org/W2129297793","https://openalex.org/W2131479143","https://openalex.org/W2138228305","https://openalex.org/W2159023782","https://openalex.org/W2165644552","https://openalex.org/W2166721725","https://openalex.org/W2394550695","https://openalex.org/W2561587488","https://openalex.org/W2604687138","https://openalex.org/W2753709519","https://openalex.org/W2755962456","https://openalex.org/W2767286446","https://openalex.org/W2792270354","https://openalex.org/W2913340405","https://openalex.org/W2923380536","https://openalex.org/W2949664970","https://openalex.org/W2952505042","https://openalex.org/W2953742623","https://openalex.org/W2962708900","https://openalex.org/W2963722008","https://openalex.org/W2963877604","https://openalex.org/W2964121793","https://openalex.org/W2981720610","https://openalex.org/W3087549734","https://openalex.org/W3118867567","https://openalex.org/W3141797743","https://openalex.org/W3168230249","https://openalex.org/W3201004487","https://openalex.org/W6631190155","https://openalex.org/W6636918297","https://openalex.org/W6637373629","https://openalex.org/W6640290990","https://openalex.org/W6640786210","https://openalex.org/W6676310258","https://openalex.org/W6677658955","https://openalex.org/W6679363028","https://openalex.org/W6679468046","https://openalex.org/W6683539301","https://openalex.org/W6684360038","https://openalex.org/W6684671274","https://openalex.org/W6713960836","https://openalex.org/W6718046261","https://openalex.org/W6745996482","https://openalex.org/W6751523768","https://openalex.org/W6783292247","https://openalex.org/W6801912494"],"related_works":["https://openalex.org/W2569661359","https://openalex.org/W2781510240","https://openalex.org/W2950186459","https://openalex.org/W2170114491","https://openalex.org/W2897298721","https://openalex.org/W2242624680","https://openalex.org/W2136127937","https://openalex.org/W4290987221","https://openalex.org/W2216309014","https://openalex.org/W3199841771"],"abstract_inverted_index":{"As":[0],"the":[1,21,26,31,38,68,80,86,95,101,138,143,147,152,163,168,179,186,189],"trace":[2,45,57,89,125],"norm":[3],"can":[4,135],"discover":[5,66,136],"low-rank":[6,22,69,97,140,175],"structures":[7,70],"in":[8,16,25,71,85,104,146,162],"a":[9,72,109,120],"matrix,":[10],"it":[11,134],"has":[12],"been":[13,48],"widely":[14],"used":[15,49],"multi-task":[17,52],"learning":[18,42],"to":[19,77,158,172],"recover":[20],"structure":[23,176],"contained":[24],"parameter":[27],"matrix.":[28],"Recently,":[29],"with":[30,151],"emerging":[32],"of":[33,40,82,123,127,188],"big":[34],"complex":[35],"datasets":[36,184],"and":[37,91,132,192],"popularity":[39],"deep":[41,51],"techniques,":[43],"tensor":[44,56,88,130],"norms":[46,58,126],"have":[47],"for":[50],"models.":[53],"However,":[54],"existing":[55],"exhibit":[59],"some":[60],"limitations.":[61],"For":[62,142],"example,":[63],"they":[64,74,92],"cannot":[65],"all":[67,128,137,178],"tensor,":[73],"require":[75],"users":[76],"manually":[78],"specify":[79],"importance":[81],"each":[83],"component":[84],"corresponding":[87],"norm,":[90],"only":[93],"capture":[94,173],"linear":[96],"structure.":[98],"To":[99],"solve":[100],"first":[102],"issue,":[103,145],"this":[105],"paper,":[106],"we":[107,154,166],"propose":[108,155,167],"Generalized":[110],"Tensor":[111],"Trace":[112],"Norm":[113],"(GTTN).":[114],"The":[115],"GTTN":[116,170,191],"is":[117],"defined":[118],"as":[119],"convex":[121],"combination":[122,160],"matrix":[124],"possible":[129,139],"flattenings":[131],"hence":[133],"structures.":[141],"second":[144],"induced":[148],"objective":[149],"function":[150],"GTTN,":[153],"four":[156],"strategies":[157],"learn":[159],"coefficients":[161],"GTTN.":[164],"Furthermore,":[165],"Nonlinear":[169],"(NGTTN)":[171],"nonlinear":[174],"among":[177],"tasks.":[180],"Experiments":[181],"on":[182],"benchmark":[183],"demonstrate":[185],"effectiveness":[187],"proposed":[190],"NGTTN.":[193]},"counts_by_year":[{"year":2024,"cited_by_count":3},{"year":2022,"cited_by_count":2}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}