{"id":"https://openalex.org/W4383888353","doi":"https://doi.org/10.1162/neco_a_01598","title":"Graph-Regularized Tensor Regression: A Domain-Aware Framework for Interpretable Modeling of Multiway Data on Graphs","display_name":"Graph-Regularized Tensor Regression: A Domain-Aware Framework for Interpretable Modeling of Multiway Data on Graphs","publication_year":2023,"publication_date":"2023-07-11","ids":{"openalex":"https://openalex.org/W4383888353","doi":"https://doi.org/10.1162/neco_a_01598","pmid":"https://pubmed.ncbi.nlm.nih.gov/37432867"},"language":"en","primary_location":{"id":"doi:10.1162/neco_a_01598","is_oa":false,"landing_page_url":"https://doi.org/10.1162/neco_a_01598","pdf_url":null,"source":{"id":"https://openalex.org/S207023548","display_name":"Neural Computation","issn_l":"0899-7667","issn":["0899-7667","1530-888X"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310315718","host_organization_name":"The MIT Press","host_organization_lineage":["https://openalex.org/P4310315718"],"host_organization_lineage_names":["The MIT Press"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Neural Computation","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","pubmed"],"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/A5053802268","display_name":"Yao Xu","orcid":"https://orcid.org/0000-0003-3315-2070"},"institutions":[{"id":"https://openalex.org/I47508984","display_name":"Imperial College London","ror":"https://ror.org/041kmwe10","country_code":"GB","type":"education","lineage":["https://openalex.org/I47508984"]}],"countries":["GB"],"is_corresponding":true,"raw_author_name":"Yao Lei Xu","raw_affiliation_strings":["Department of Electrical and Electronic Engineering, Imperial College London, London SW7 2AZ, U.K. yao.xu15@imperial.ac.uk"],"affiliations":[{"raw_affiliation_string":"Department of Electrical and Electronic Engineering, Imperial College London, London SW7 2AZ, U.K. yao.xu15@imperial.ac.uk","institution_ids":["https://openalex.org/I47508984"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5064995689","display_name":"Kriton Konstantinidis","orcid":null},"institutions":[{"id":"https://openalex.org/I47508984","display_name":"Imperial College London","ror":"https://ror.org/041kmwe10","country_code":"GB","type":"education","lineage":["https://openalex.org/I47508984"]}],"countries":["GB"],"is_corresponding":true,"raw_author_name":"Kriton Konstantinidis","raw_affiliation_strings":["Department of Electrical and Electronic Engineering, Imperial College London, London SW7 2AZ, U.K. k.konstantinidis19@imperial.ac.uk"],"affiliations":[{"raw_affiliation_string":"Department of Electrical and Electronic Engineering, Imperial College London, London SW7 2AZ, U.K. k.konstantinidis19@imperial.ac.uk","institution_ids":["https://openalex.org/I47508984"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5103001848","display_name":"Danilo P. Mandic","orcid":"https://orcid.org/0000-0001-8432-3963"},"institutions":[{"id":"https://openalex.org/I47508984","display_name":"Imperial College London","ror":"https://ror.org/041kmwe10","country_code":"GB","type":"education","lineage":["https://openalex.org/I47508984"]}],"countries":["GB"],"is_corresponding":true,"raw_author_name":"Danilo P. Mandic","raw_affiliation_strings":["Department of Electrical and Electronic Engineering, Imperial College London, London SW7 2AZ, U.K. d.mandic@imperial.ac.uk"],"affiliations":[{"raw_affiliation_string":"Department of Electrical and Electronic Engineering, Imperial College London, London SW7 2AZ, U.K. d.mandic@imperial.ac.uk","institution_ids":["https://openalex.org/I47508984"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5053802268","https://openalex.org/A5064995689","https://openalex.org/A5103001848"],"corresponding_institution_ids":["https://openalex.org/I47508984"],"apc_list":null,"apc_paid":null,"fwci":0.4334,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.52777778,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":95},"biblio":{"volume":"35","issue":"8","first_page":"1404","last_page":"1429"},"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"}}],"keywords":[{"id":"https://openalex.org/keywords/tensor","display_name":"Tensor (intrinsic definition)","score":0.6934974193572998},{"id":"https://openalex.org/keywords/dimensionality-reduction","display_name":"Dimensionality reduction","score":0.6683703660964966},{"id":"https://openalex.org/keywords/curse-of-dimensionality","display_name":"Curse of dimensionality","score":0.632897675037384},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5718903541564941},{"id":"https://openalex.org/keywords/regularization","display_name":"Regularization (linguistics)","score":0.5595800876617432},{"id":"https://openalex.org/keywords/interpretability","display_name":"Interpretability","score":0.4661727249622345},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4558784067630768},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.4414827525615692},{"id":"https://openalex.org/keywords/regression","display_name":"Regression","score":0.42239993810653687},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.40762513875961304},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.32779374718666077}],"concepts":[{"id":"https://openalex.org/C155281189","wikidata":"https://www.wikidata.org/wiki/Q3518150","display_name":"Tensor (intrinsic definition)","level":2,"score":0.6934974193572998},{"id":"https://openalex.org/C70518039","wikidata":"https://www.wikidata.org/wiki/Q16000077","display_name":"Dimensionality reduction","level":2,"score":0.6683703660964966},{"id":"https://openalex.org/C111030470","wikidata":"https://www.wikidata.org/wiki/Q1430460","display_name":"Curse of dimensionality","level":2,"score":0.632897675037384},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5718903541564941},{"id":"https://openalex.org/C2776135515","wikidata":"https://www.wikidata.org/wiki/Q17143721","display_name":"Regularization (linguistics)","level":2,"score":0.5595800876617432},{"id":"https://openalex.org/C2781067378","wikidata":"https://www.wikidata.org/wiki/Q17027399","display_name":"Interpretability","level":2,"score":0.4661727249622345},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4558784067630768},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.4414827525615692},{"id":"https://openalex.org/C83546350","wikidata":"https://www.wikidata.org/wiki/Q1139051","display_name":"Regression","level":2,"score":0.42239993810653687},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.40762513875961304},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.32779374718666077},{"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/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1162/neco_a_01598","is_oa":false,"landing_page_url":"https://doi.org/10.1162/neco_a_01598","pdf_url":null,"source":{"id":"https://openalex.org/S207023548","display_name":"Neural Computation","issn_l":"0899-7667","issn":["0899-7667","1530-888X"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310315718","host_organization_name":"The MIT Press","host_organization_lineage":["https://openalex.org/P4310315718"],"host_organization_lineage_names":["The MIT Press"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Neural Computation","raw_type":"journal-article"},{"id":"pmid:37432867","is_oa":false,"landing_page_url":"https://pubmed.ncbi.nlm.nih.gov/37432867","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":"Neural computation","raw_type":null}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":36,"referenced_works":["https://openalex.org/W1580176742","https://openalex.org/W1970176196","https://openalex.org/W2025603201","https://openalex.org/W2119412403","https://openalex.org/W2136002544","https://openalex.org/W2144913102","https://openalex.org/W2330159389","https://openalex.org/W2516041031","https://openalex.org/W2559655401","https://openalex.org/W2617994470","https://openalex.org/W2623012778","https://openalex.org/W2753090725","https://openalex.org/W2754880706","https://openalex.org/W2982697215","https://openalex.org/W3006217815","https://openalex.org/W3016075648","https://openalex.org/W3016132608","https://openalex.org/W3105291792","https://openalex.org/W3107718410","https://openalex.org/W3107724517","https://openalex.org/W3108029334","https://openalex.org/W3122280285","https://openalex.org/W3150160418","https://openalex.org/W3203920103","https://openalex.org/W3215602957","https://openalex.org/W4205706318","https://openalex.org/W4225323172","https://openalex.org/W4234552385","https://openalex.org/W4244493851","https://openalex.org/W6638060716","https://openalex.org/W6638909124","https://openalex.org/W6697202215","https://openalex.org/W6728790633","https://openalex.org/W6729942461","https://openalex.org/W6748780714","https://openalex.org/W6784855270"],"related_works":["https://openalex.org/W1995622179","https://openalex.org/W1484111231","https://openalex.org/W1552543208","https://openalex.org/W2074396517","https://openalex.org/W2166963679","https://openalex.org/W2187269125","https://openalex.org/W1641615907","https://openalex.org/W3089231081","https://openalex.org/W2093956241","https://openalex.org/W2354420595"],"abstract_inverted_index":{"Modern":[0],"data":[1,12,33,38],"analytics":[2],"applications":[3],"are":[4,74,182],"increasingly":[5],"characterized":[6],"by":[7],"exceedingly":[8],"large":[9],"and":[10,152,164,169],"multidimensional":[11],"sources.":[13],"This":[14,118],"represents":[15],"a":[16,92,114,123,128,160],"challenge":[17],"for":[18],"traditional":[19],"machine":[20],"learning":[21],"models,":[22],"as":[23,43,122],"the":[24,37,44,59,79,108,111,133,141,192],"number":[25],"of":[26,46,113,138,191],"model":[27,109,134,156],"parameters":[28],"needed":[29],"to":[30,77,126,146,172,184],"process":[31],"such":[32,71],"grows":[34],"exponentially":[35],"with":[36,63],"dimensions,":[39],"an":[40,188],"effect":[41],"known":[42],"curse":[45],"dimensionality.":[47],"Recently,":[48],"tensor":[49,72,95,139,194],"decomposition":[50],"(TD)":[51],"techniques":[52],"have":[53],"shown":[54,145,171],"promising":[55],"results":[56],"in":[57,110,159],"reducing":[58],"computational":[60,178],"costs":[61],"associated":[62],"large-dimensional":[64],"models":[65,73,168],"while":[66],"achieving":[67],"comparable":[68],"performance.":[69],"However,":[70],"often":[75],"unable":[76],"incorporate":[78],"underlying":[80],"domain":[81,100],"knowledge":[82,101],"when":[83],"compressing":[84],"high-dimensional":[85],"models.":[86],"To":[87],"this":[88],"end,":[89],"we":[90],"introduce":[91],"novel":[93],"graph-regularized":[94],"regression":[96,162],"(GRTR)":[97],"framework,":[98],"whereby":[99],"about":[102],"intramodal":[103],"relations":[104],"is":[105,119,144,157,170],"incorporated":[106],"into":[107],"form":[112],"graph":[115],"Laplacian":[116],"matrix.":[117],"then":[120],"used":[121],"regularization":[124],"tool":[125],"promote":[127],"physically":[129],"meaningful":[130],"structure":[131],"within":[132],"parameters.":[135],"By":[136],"virtue":[137],"algebra,":[140],"proposed":[142],"framework":[143],"be":[147],"fully":[148],"interpretable,":[149],"both":[150],"coefficient-wise":[151],"dimension-wise.":[153],"The":[154],"GRTR":[155],"validated":[158],"multiway":[161],"setting":[163],"compared":[165],"against":[166],"competing":[167],"achieve":[173],"improved":[174],"performance":[175],"at":[176],"reduced":[177],"costs.":[179],"Detailed":[180],"visualizations":[181],"provided":[183],"help":[185],"readers":[186],"gain":[187],"intuitive":[189],"understanding":[190],"employed":[193],"operations.":[195]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}