{"id":"https://openalex.org/W3121108338","doi":"https://doi.org/10.23919/eusipco54536.2021.9616222","title":"A Bayesian Approach to Block-Term Tensor Decomposition Model Selection and Computation","display_name":"A Bayesian Approach to Block-Term Tensor Decomposition Model Selection and Computation","publication_year":2021,"publication_date":"2021-08-23","ids":{"openalex":"https://openalex.org/W3121108338","doi":"https://doi.org/10.23919/eusipco54536.2021.9616222","mag":"3121108338"},"language":"en","primary_location":{"id":"doi:10.23919/eusipco54536.2021.9616222","is_oa":false,"landing_page_url":"https://doi.org/10.23919/eusipco54536.2021.9616222","pdf_url":null,"source":{"id":"https://openalex.org/S4363607854","display_name":"2021 29th European Signal Processing Conference (EUSIPCO)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 29th European Signal Processing Conference (EUSIPCO)","raw_type":"proceedings-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/A5075524987","display_name":"Paris V. Giampouras","orcid":"https://orcid.org/0000-0003-2039-0758"},"institutions":[{"id":"https://openalex.org/I145311948","display_name":"Johns Hopkins University","ror":"https://ror.org/00za53h95","country_code":"US","type":"education","lineage":["https://openalex.org/I145311948"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Paris V. Giampouras","raw_affiliation_strings":["Mathematical Inst. for Data Science, Johns Hopkins University, Baltimore, MD, USA"],"affiliations":[{"raw_affiliation_string":"Mathematical Inst. for Data Science, Johns Hopkins University, Baltimore, MD, USA","institution_ids":["https://openalex.org/I145311948"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5081131282","display_name":"Athanasios A. Rontogiannis","orcid":"https://orcid.org/0000-0001-6161-4055"},"institutions":[{"id":"https://openalex.org/I4210118731","display_name":"National Observatory of Athens","ror":"https://ror.org/03dtebk39","country_code":"GR","type":"facility","lineage":["https://openalex.org/I4210118731"]}],"countries":["GR"],"is_corresponding":false,"raw_author_name":"Athanasios A. Rontogiannis","raw_affiliation_strings":["IAASARS, National Observatory of Athens,Greece"],"affiliations":[{"raw_affiliation_string":"IAASARS, National Observatory of Athens,Greece","institution_ids":["https://openalex.org/I4210118731"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5044599816","display_name":"Eleftherios Kofidis","orcid":"https://orcid.org/0000-0002-5887-9404"},"institutions":[{"id":"https://openalex.org/I154757721","display_name":"University of Piraeus","ror":"https://ror.org/02qs84g94","country_code":"GR","type":"education","lineage":["https://openalex.org/I154757721"]}],"countries":["GR"],"is_corresponding":false,"raw_author_name":"Eleftherios Kofidis","raw_affiliation_strings":["Dept. of Statistics and Insurance Science, University of Piraeus, Greece"],"affiliations":[{"raw_affiliation_string":"Dept. of Statistics and Insurance Science, University of Piraeus, Greece","institution_ids":["https://openalex.org/I154757721"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5075524987"],"corresponding_institution_ids":["https://openalex.org/I145311948"],"apc_list":null,"apc_paid":null,"fwci":0.6316,"has_fulltext":false,"cited_by_count":4,"citation_normalized_percentile":{"value":0.70769231,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":96},"biblio":{"volume":null,"issue":null,"first_page":"1800","last_page":"1804"},"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/T11447","display_name":"Blind Source Separation Techniques","score":0.9988999962806702,"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"}},{"id":"https://openalex.org/T10500","display_name":"Sparse and Compressive Sensing Techniques","score":0.9955000281333923,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/bayesian-inference","display_name":"Bayesian inference","score":0.5787834525108337},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.5772563815116882},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5682012438774109},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.5637441277503967},{"id":"https://openalex.org/keywords/model-selection","display_name":"Model selection","score":0.5309958457946777},{"id":"https://openalex.org/keywords/regularization","display_name":"Regularization (linguistics)","score":0.5279589891433716},{"id":"https://openalex.org/keywords/rank","display_name":"Rank (graph theory)","score":0.498060941696167},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.46341437101364136},{"id":"https://openalex.org/keywords/tensor","display_name":"Tensor (intrinsic definition)","score":0.45557019114494324},{"id":"https://openalex.org/keywords/computation","display_name":"Computation","score":0.4421156644821167},{"id":"https://openalex.org/keywords/block","display_name":"Block (permutation group theory)","score":0.4323952794075012},{"id":"https://openalex.org/keywords/selection","display_name":"Selection (genetic algorithm)","score":0.4250262379646301},{"id":"https://openalex.org/keywords/uniqueness","display_name":"Uniqueness","score":0.42459022998809814},{"id":"https://openalex.org/keywords/approximate-bayesian-computation","display_name":"Approximate Bayesian computation","score":0.41512298583984375},{"id":"https://openalex.org/keywords/term","display_name":"Term (time)","score":0.41140320897102356},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3706139326095581},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.34970080852508545},{"id":"https://openalex.org/keywords/combinatorics","display_name":"Combinatorics","score":0.08808165788650513}],"concepts":[{"id":"https://openalex.org/C160234255","wikidata":"https://www.wikidata.org/wiki/Q812535","display_name":"Bayesian inference","level":3,"score":0.5787834525108337},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.5772563815116882},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5682012438774109},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.5637441277503967},{"id":"https://openalex.org/C93959086","wikidata":"https://www.wikidata.org/wiki/Q6888345","display_name":"Model selection","level":2,"score":0.5309958457946777},{"id":"https://openalex.org/C2776135515","wikidata":"https://www.wikidata.org/wiki/Q17143721","display_name":"Regularization (linguistics)","level":2,"score":0.5279589891433716},{"id":"https://openalex.org/C164226766","wikidata":"https://www.wikidata.org/wiki/Q7293202","display_name":"Rank (graph theory)","level":2,"score":0.498060941696167},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.46341437101364136},{"id":"https://openalex.org/C155281189","wikidata":"https://www.wikidata.org/wiki/Q3518150","display_name":"Tensor (intrinsic definition)","level":2,"score":0.45557019114494324},{"id":"https://openalex.org/C45374587","wikidata":"https://www.wikidata.org/wiki/Q12525525","display_name":"Computation","level":2,"score":0.4421156644821167},{"id":"https://openalex.org/C2777210771","wikidata":"https://www.wikidata.org/wiki/Q4927124","display_name":"Block (permutation group theory)","level":2,"score":0.4323952794075012},{"id":"https://openalex.org/C81917197","wikidata":"https://www.wikidata.org/wiki/Q628760","display_name":"Selection (genetic algorithm)","level":2,"score":0.4250262379646301},{"id":"https://openalex.org/C2777021972","wikidata":"https://www.wikidata.org/wiki/Q22976830","display_name":"Uniqueness","level":2,"score":0.42459022998809814},{"id":"https://openalex.org/C2779377595","wikidata":"https://www.wikidata.org/wiki/Q21045424","display_name":"Approximate Bayesian computation","level":3,"score":0.41512298583984375},{"id":"https://openalex.org/C61797465","wikidata":"https://www.wikidata.org/wiki/Q1188986","display_name":"Term (time)","level":2,"score":0.41140320897102356},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3706139326095581},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.34970080852508545},{"id":"https://openalex.org/C114614502","wikidata":"https://www.wikidata.org/wiki/Q76592","display_name":"Combinatorics","level":1,"score":0.08808165788650513},{"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/C202444582","wikidata":"https://www.wikidata.org/wiki/Q837863","display_name":"Pure mathematics","level":1,"score":0.0},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","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}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.23919/eusipco54536.2021.9616222","is_oa":false,"landing_page_url":"https://doi.org/10.23919/eusipco54536.2021.9616222","pdf_url":null,"source":{"id":"https://openalex.org/S4363607854","display_name":"2021 29th European Signal Processing Conference (EUSIPCO)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 29th European Signal Processing Conference (EUSIPCO)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":11,"referenced_works":["https://openalex.org/W165197643","https://openalex.org/W368469426","https://openalex.org/W1881945180","https://openalex.org/W2000045479","https://openalex.org/W2000721204","https://openalex.org/W2566771666","https://openalex.org/W2586363583","https://openalex.org/W3013121531","https://openalex.org/W3083628794","https://openalex.org/W3093425554","https://openalex.org/W3121108338"],"related_works":["https://openalex.org/W3012988968","https://openalex.org/W4287824571","https://openalex.org/W2971727066","https://openalex.org/W3113268434","https://openalex.org/W4362583294","https://openalex.org/W2966726156","https://openalex.org/W4242918646","https://openalex.org/W4289242714","https://openalex.org/W3159703654","https://openalex.org/W2902432378"],"abstract_inverted_index":{"The":[0],"so-called":[1],"block-term":[2],"decomposition":[3],"(BTD)":[4],"tensor":[5],"model,":[6],"especially":[7],"in":[8,47,126,174,196],"its":[9,26],"rank":[10,40,102,200],"<tex":[11,103],"xmlns:mml=\"http://www.w3.org/1998/Math/MathML\"":[12,104],"xmlns:xlink=\"http://www.w3.org/1999/xlink\">$-(L_{r},":[13],"L_{r},":[14],"1)$</tex>":[15,107],",":[16],"version,":[17],"has":[18,81],"been":[19,58],"recently":[20,83],"receiving":[21],"increasing":[22],"attention":[23],"due":[24],"to":[25,85,97,159],"enhanced":[27],"ability":[28],"of":[29,37,39,65,74,101,117,137,140,178,192,198],"representing":[30],"systems":[31],"and":[32,49,54,77,111,125,130,202],"signals":[33],"that":[34,163],"are":[35,186],"composed":[36],"blocks":[38],"higher":[41],"than":[42],"one,":[43],"a":[44,92,127],"scenario":[45],"encountered":[46],"numerous":[48],"diverse":[50],"applications.":[51],"Its":[52,167],"uniqueness":[53],"approximation":[55],"have":[56],"thus":[57],"thoroughly":[59],"studied.":[60],"Nevertheless,":[61],"the":[62,67,72,99,115,123,132,135,147,172,190,193],"challenging":[63],"problem":[64,100],"estimating":[66,131],"BTD":[68,108],"model":[69,109,151,203],"structure,":[70],"namely":[71],"number":[73],"block":[75],"terms":[76,197],"their":[78],"individual":[79],"ranks,":[80],"only":[82],"started":[84],"attract":[86],"significant":[87],"attention.":[88],"In":[89],"this":[90],"work,":[91],"Bayesian":[93,150,168],"approach":[94],"is":[95,152],"taken":[96],"addressing":[98],"xmlns:xlink=\"http://www.w3.org/1999/xlink\">$(L_{r},\\":[105],"L_{r},\\":[106],"selection":[110],"computation,":[112],"based":[113,153],"on":[114,122,154],"idea":[116],"imposing":[118],"column":[119],"sparsity":[120],"jointly":[121],"factors":[124],"hierarchical":[128],"manner":[129],"ranks":[133],"as":[134],"numbers":[136],"factor":[138],"columns":[139],"non-negligible":[141],"energy.":[142],"Approximate":[143],"posterior":[144],"inference":[145,156],"for":[146],"proposed":[148,194],"sophisticated":[149],"variational":[155],"giving":[157],"rise":[158],"an":[160],"iterative":[161],"algorithm":[162],"comprises":[164],"closed-form":[165],"updates.":[166],"nature":[169],"completely":[170],"avoids":[171],"ubiquitous":[173],"regularization-based":[175],"methods":[176],"task":[177],"hyper-parameter":[179],"tuning.":[180],"Simulation":[181],"results":[182],"with":[183],"synthetic":[184],"data":[185],"reported,":[187],"which":[188],"demonstrate":[189],"effectiveness":[191],"scheme":[195],"both":[199],"estimation":[201],"fitting.":[204]},"counts_by_year":[{"year":2023,"cited_by_count":2},{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}