{"id":"https://openalex.org/W3193291542","doi":"https://doi.org/10.1080/10618600.2021.1978471","title":"Generalized Tensor Decomposition With Features on Multiple Modes","display_name":"Generalized Tensor Decomposition With Features on Multiple Modes","publication_year":2021,"publication_date":"2021-09-14","ids":{"openalex":"https://openalex.org/W3193291542","doi":"https://doi.org/10.1080/10618600.2021.1978471","mag":"3193291542"},"language":"en","primary_location":{"id":"doi:10.1080/10618600.2021.1978471","is_oa":false,"landing_page_url":"https://doi.org/10.1080/10618600.2021.1978471","pdf_url":null,"source":{"id":"https://openalex.org/S76159266","display_name":"Journal of Computational and Graphical Statistics","issn_l":"1061-8600","issn":["1061-8600","1537-2715"],"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":"Journal of Computational and Graphical Statistics","raw_type":"journal-article"},"type":"article","indexed_in":["arxiv","crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/1910.09499","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"Jiaxin Hu","orcid":null},"institutions":[{"id":"https://openalex.org/I135310074","display_name":"University of Wisconsin\u2013Madison","ror":"https://ror.org/01y2jtd41","country_code":"US","type":"education","lineage":["https://openalex.org/I135310074"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Jiaxin Hu","raw_affiliation_strings":["Department of Statistics, University of Wisconsin-Madison, Madison, WI"],"affiliations":[{"raw_affiliation_string":"Department of Statistics, University of Wisconsin-Madison, Madison, WI","institution_ids":["https://openalex.org/I135310074"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Chanwoo Lee","orcid":null},"institutions":[{"id":"https://openalex.org/I135310074","display_name":"University of Wisconsin\u2013Madison","ror":"https://ror.org/01y2jtd41","country_code":"US","type":"education","lineage":["https://openalex.org/I135310074"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Chanwoo Lee","raw_affiliation_strings":["Department of Statistics, University of Wisconsin-Madison, Madison, WI"],"affiliations":[{"raw_affiliation_string":"Department of Statistics, University of Wisconsin-Madison, Madison, WI","institution_ids":["https://openalex.org/I135310074"]}]},{"author_position":"last","author":{"id":null,"display_name":"Miaoyan Wang","orcid":null},"institutions":[{"id":"https://openalex.org/I135310074","display_name":"University of Wisconsin\u2013Madison","ror":"https://ror.org/01y2jtd41","country_code":"US","type":"education","lineage":["https://openalex.org/I135310074"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Miaoyan Wang","raw_affiliation_strings":["Department of Statistics, University of Wisconsin-Madison, Madison, WI"],"affiliations":[{"raw_affiliation_string":"Department of Statistics, University of Wisconsin-Madison, Madison, WI","institution_ids":["https://openalex.org/I135310074"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I135310074"],"apc_list":null,"apc_paid":null,"fwci":0.9661,"has_fulltext":false,"cited_by_count":8,"citation_normalized_percentile":{"value":0.72340426,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":91,"max":98},"biblio":{"volume":"31","issue":"1","first_page":"204","last_page":"218"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12303","display_name":"Tensor decomposition and applications","score":0.5687000155448914,"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":0.5687000155448914,"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/T10241","display_name":"Functional Brain Connectivity Studies","score":0.19020000100135803,"subfield":{"id":"https://openalex.org/subfields/2805","display_name":"Cognitive Neuroscience"},"field":{"id":"https://openalex.org/fields/28","display_name":"Neuroscience"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},{"id":"https://openalex.org/T11304","display_name":"Advanced Neuroimaging Techniques and Applications","score":0.18690000474452972,"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/tensor","display_name":"Tensor (intrinsic definition)","score":0.7106999754905701},{"id":"https://openalex.org/keywords/initialization","display_name":"Initialization","score":0.6643000245094299},{"id":"https://openalex.org/keywords/matrix-decomposition","display_name":"Matrix decomposition","score":0.4765999913215637},{"id":"https://openalex.org/keywords/tensor-decomposition","display_name":"Tensor decomposition","score":0.4560999870300293},{"id":"https://openalex.org/keywords/dimensionality-reduction","display_name":"Dimensionality reduction","score":0.45089998841285706},{"id":"https://openalex.org/keywords/diffusion-mri","display_name":"Diffusion MRI","score":0.4275999963283539},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.3686999976634979},{"id":"https://openalex.org/keywords/cartesian-tensor","display_name":"Cartesian tensor","score":0.35929998755455017},{"id":"https://openalex.org/keywords/decomposition","display_name":"Decomposition","score":0.35339999198913574},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.34290000796318054}],"concepts":[{"id":"https://openalex.org/C155281189","wikidata":"https://www.wikidata.org/wiki/Q3518150","display_name":"Tensor (intrinsic definition)","level":2,"score":0.7106999754905701},{"id":"https://openalex.org/C114466953","wikidata":"https://www.wikidata.org/wiki/Q6034165","display_name":"Initialization","level":2,"score":0.6643000245094299},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.49570000171661377},{"id":"https://openalex.org/C42355184","wikidata":"https://www.wikidata.org/wiki/Q1361088","display_name":"Matrix decomposition","level":3,"score":0.4765999913215637},{"id":"https://openalex.org/C2986737658","wikidata":"https://www.wikidata.org/wiki/Q30103009","display_name":"Tensor decomposition","level":3,"score":0.4560999870300293},{"id":"https://openalex.org/C70518039","wikidata":"https://www.wikidata.org/wiki/Q16000077","display_name":"Dimensionality reduction","level":2,"score":0.45089998841285706},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.43130001425743103},{"id":"https://openalex.org/C149550507","wikidata":"https://www.wikidata.org/wiki/Q899360","display_name":"Diffusion MRI","level":3,"score":0.4275999963283539},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3741999864578247},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.3686999976634979},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.36169999837875366},{"id":"https://openalex.org/C64835786","wikidata":"https://www.wikidata.org/wiki/Q17004583","display_name":"Cartesian tensor","level":5,"score":0.35929998755455017},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3578999936580658},{"id":"https://openalex.org/C124681953","wikidata":"https://www.wikidata.org/wiki/Q339062","display_name":"Decomposition","level":2,"score":0.35339999198913574},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.34290000796318054},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.33880001306533813},{"id":"https://openalex.org/C9357733","wikidata":"https://www.wikidata.org/wiki/Q6878417","display_name":"Missing data","level":2,"score":0.337799996137619},{"id":"https://openalex.org/C75684735","wikidata":"https://www.wikidata.org/wiki/Q858810","display_name":"Big data","level":2,"score":0.3224000036716461},{"id":"https://openalex.org/C62611344","wikidata":"https://www.wikidata.org/wiki/Q1062658","display_name":"Node (physics)","level":2,"score":0.31709998846054077},{"id":"https://openalex.org/C33676613","wikidata":"https://www.wikidata.org/wiki/Q13415176","display_name":"Dimension (graph theory)","level":2,"score":0.31209999322891235},{"id":"https://openalex.org/C2779190172","wikidata":"https://www.wikidata.org/wiki/Q4913888","display_name":"Binary data","level":3,"score":0.302700012922287},{"id":"https://openalex.org/C2778572836","wikidata":"https://www.wikidata.org/wiki/Q380933","display_name":"Space (punctuation)","level":2,"score":0.29159998893737793},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.28600001335144043},{"id":"https://openalex.org/C148483581","wikidata":"https://www.wikidata.org/wiki/Q446488","display_name":"Feature selection","level":2,"score":0.2842999994754791},{"id":"https://openalex.org/C160920958","wikidata":"https://www.wikidata.org/wiki/Q7662746","display_name":"Synthetic data","level":2,"score":0.2838999927043915},{"id":"https://openalex.org/C83665646","wikidata":"https://www.wikidata.org/wiki/Q42139305","display_name":"Feature vector","level":2,"score":0.2768999934196472},{"id":"https://openalex.org/C20178491","wikidata":"https://www.wikidata.org/wiki/Q2204117","display_name":"Symmetric tensor","level":3,"score":0.2761000096797943},{"id":"https://openalex.org/C42704193","wikidata":"https://www.wikidata.org/wiki/Q7851097","display_name":"Tucker decomposition","level":4,"score":0.2736999988555908},{"id":"https://openalex.org/C106487976","wikidata":"https://www.wikidata.org/wiki/Q685816","display_name":"Matrix (chemical analysis)","level":2,"score":0.27149999141693115},{"id":"https://openalex.org/C2776029896","wikidata":"https://www.wikidata.org/wiki/Q3935810","display_name":"Relaxation (psychology)","level":2,"score":0.26339998841285706},{"id":"https://openalex.org/C84787856","wikidata":"https://www.wikidata.org/wiki/Q3076659","display_name":"Tractography","level":4,"score":0.2624000012874603},{"id":"https://openalex.org/C138958017","wikidata":"https://www.wikidata.org/wiki/Q190087","display_name":"Data type","level":2,"score":0.2621000111103058},{"id":"https://openalex.org/C204323151","wikidata":"https://www.wikidata.org/wiki/Q905424","display_name":"Range (aeronautics)","level":2,"score":0.26089999079704285},{"id":"https://openalex.org/C137836250","wikidata":"https://www.wikidata.org/wiki/Q984063","display_name":"Optimization problem","level":2,"score":0.26030001044273376},{"id":"https://openalex.org/C111335779","wikidata":"https://www.wikidata.org/wiki/Q3454686","display_name":"Reduction (mathematics)","level":2,"score":0.2540000081062317},{"id":"https://openalex.org/C136389625","wikidata":"https://www.wikidata.org/wiki/Q334384","display_name":"Supervised learning","level":3,"score":0.2533999979496002},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.2513999938964844},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.25119999051094055}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1080/10618600.2021.1978471","is_oa":false,"landing_page_url":"https://doi.org/10.1080/10618600.2021.1978471","pdf_url":null,"source":{"id":"https://openalex.org/S76159266","display_name":"Journal of Computational and Graphical Statistics","issn_l":"1061-8600","issn":["1061-8600","1537-2715"],"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":"Journal of Computational and Graphical Statistics","raw_type":"journal-article"},{"id":"pmh:oai:arXiv.org:1910.09499","is_oa":true,"landing_page_url":"http://arxiv.org/abs/1910.09499","pdf_url":"https://arxiv.org/pdf/1910.09499","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"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:1910.09499","is_oa":true,"landing_page_url":"http://arxiv.org/abs/1910.09499","pdf_url":"https://arxiv.org/pdf/1910.09499","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":[],"funders":[{"id":"https://openalex.org/F4320337380","display_name":"Division of Mathematical Sciences","ror":"https://ror.org/051fftw81"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":32,"referenced_works":["https://openalex.org/W1532814032","https://openalex.org/W1995945562","https://openalex.org/W2009872283","https://openalex.org/W2009922756","https://openalex.org/W2013912476","https://openalex.org/W2022215192","https://openalex.org/W2024165284","https://openalex.org/W2024356620","https://openalex.org/W2024729467","https://openalex.org/W2065760681","https://openalex.org/W2101135654","https://openalex.org/W2123082179","https://openalex.org/W2136002544","https://openalex.org/W2142081666","https://openalex.org/W2569250433","https://openalex.org/W2574800895","https://openalex.org/W2726993892","https://openalex.org/W2810050069","https://openalex.org/W2894698273","https://openalex.org/W2944320443","https://openalex.org/W2951293558","https://openalex.org/W2963472624","https://openalex.org/W2963924474","https://openalex.org/W2982569731","https://openalex.org/W2991313168","https://openalex.org/W3005990752","https://openalex.org/W3022847472","https://openalex.org/W3037066305","https://openalex.org/W3106376083","https://openalex.org/W3127333884","https://openalex.org/W3157274685","https://openalex.org/W4301861531"],"related_works":[],"abstract_inverted_index":{"Higher-order":[0],"tensors":[1],"have":[2],"received":[3],"increased":[4],"attention":[5],"across":[6],"science":[7],"and":[8,34,51,61,129,145,157,170],"engineering.":[9],"While":[10],"most":[11],"tensor":[12,20,40,60,72,85,98,138],"decomposition":[13,73,89],"methods":[14],"are":[15,45,163,173,182],"developed":[16],"for":[17,179],"a":[18,58,71,120],"single":[19],"observation,":[21],"scientific":[22],"studies":[23],"often":[24],"collect":[25],"side":[26,62,81],"information,":[27],"in":[28,47],"the":[29,39,55,91,96,134,152,159],"form":[30],"of":[31,95,103,123],"node":[32],"features":[33],"interactions":[35],"thereof,":[36],"together":[37],"with":[38,110,165],"data.":[41,149],"Such":[42],"data":[43,97,124,140,171],"problems":[44],"common":[46],"neuroimaging,":[48],"network":[49,148],"analysis,":[50],"spatial-temporal":[52],"modeling.":[53],"Identifying":[54],"relationship":[56],"between":[57],"high-dimensional":[59],"information":[63],"is":[64,114],"important":[65],"yet":[66],"challenging.":[67],"Here,":[68],"we":[69],"develop":[70],"method":[74,135],"that":[75,162],"incorporates":[76],"multiple":[77],"feature":[78,101],"matrices":[79],"as":[80],"information.":[82],"Unlike":[83],"unsupervised":[84],"decomposition,":[86],"our":[87],"supervised":[88],"captures":[90],"effective":[92],"dimension":[93],"reduction":[94],"confined":[99],"to":[100,136],"space":[102],"interest.":[104],"An":[105],"efficient":[106],"alternating":[107],"optimization":[108],"algorithm":[109],"provable":[111],"spectral":[112],"initialization":[113],"further":[115],"developed.":[116],"Our":[117],"proposal":[118],"handles":[119],"broad":[121],"range":[122],"types,":[125],"including":[126],"continuous,":[127],"count,":[128],"binary":[130],"observations.":[131],"We":[132,150],"apply":[133],"diffusion":[137],"imaging":[139],"from":[141],"human":[142],"connectome":[143],"project":[144],"multi-relational":[146],"political":[147],"identify":[151],"key":[153],"global":[154],"connectivity":[155],"pattern":[156],"pinpoint":[158],"local":[160],"regions":[161],"associated":[164],"available":[166,174,183],"features.":[167],"The":[168],"package":[169],"used":[172],"at":[175],"https://CRAN.R-project.org/package=tensorregress.":[176],"Supplementary":[177],"materials":[178],"this":[180],"article":[181],"online.":[184]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":3},{"year":2023,"cited_by_count":4}],"updated_date":"2026-04-09T08:11:56.329763","created_date":"2021-08-30T00:00:00"}