{"id":"https://openalex.org/W2903911751","doi":"https://doi.org/10.1109/globalsip.2018.8646694","title":"TENSOR ENSEMBLE LEARNING FOR MULTIDIMENSIONAL DATA","display_name":"TENSOR ENSEMBLE LEARNING FOR MULTIDIMENSIONAL DATA","publication_year":2018,"publication_date":"2018-11-01","ids":{"openalex":"https://openalex.org/W2903911751","doi":"https://doi.org/10.1109/globalsip.2018.8646694","mag":"2903911751"},"language":"en","primary_location":{"id":"doi:10.1109/globalsip.2018.8646694","is_oa":false,"landing_page_url":"https://doi.org/10.1109/globalsip.2018.8646694","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 IEEE Global Conference on Signal and Information Processing (GlobalSIP)","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/A5001875757","display_name":"Ilia Kisil","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":"Ilia Kisil","raw_affiliation_strings":["Department of Electrical and Electronic Engineering, Imperial College, London, UK"],"affiliations":[{"raw_affiliation_string":"Department of Electrical and Electronic Engineering, Imperial College, London, UK","institution_ids":["https://openalex.org/I47508984"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5058525592","display_name":"Ahmad Moniri","orcid":"https://orcid.org/0000-0002-7184-0758"},"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":false,"raw_author_name":"Ahmad Moniri","raw_affiliation_strings":["Department of Electrical and Electronic Engineering, Imperial College, London, UK"],"affiliations":[{"raw_affiliation_string":"Department of Electrical and Electronic Engineering, Imperial College, London, 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":false,"raw_author_name":"Danilo P. Mandic","raw_affiliation_strings":["Department of Electrical and Electronic Engineering, Imperial College, London, UK"],"affiliations":[{"raw_affiliation_string":"Department of Electrical and Electronic Engineering, Imperial College, London, UK","institution_ids":["https://openalex.org/I47508984"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5001875757"],"corresponding_institution_ids":["https://openalex.org/I47508984"],"apc_list":null,"apc_paid":null,"fwci":0.3681,"has_fulltext":false,"cited_by_count":5,"citation_normalized_percentile":{"value":0.51328671,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":94},"biblio":{"volume":"12","issue":null,"first_page":"1358","last_page":"1362"},"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/T13650","display_name":"Computational Physics and Python Applications","score":0.972000002861023,"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"}},{"id":"https://openalex.org/T11233","display_name":"Advanced Adaptive Filtering Techniques","score":0.9431999921798706,"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/computer-science","display_name":"Computer science","score":0.6560561656951904},{"id":"https://openalex.org/keywords/tensor","display_name":"Tensor (intrinsic definition)","score":0.5447179079055786},{"id":"https://openalex.org/keywords/ensemble-learning","display_name":"Ensemble learning","score":0.47062328457832336},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4417647421360016},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.17435508966445923}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6560561656951904},{"id":"https://openalex.org/C155281189","wikidata":"https://www.wikidata.org/wiki/Q3518150","display_name":"Tensor (intrinsic definition)","level":2,"score":0.5447179079055786},{"id":"https://openalex.org/C45942800","wikidata":"https://www.wikidata.org/wiki/Q245652","display_name":"Ensemble learning","level":2,"score":0.47062328457832336},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4417647421360016},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.17435508966445923},{"id":"https://openalex.org/C202444582","wikidata":"https://www.wikidata.org/wiki/Q837863","display_name":"Pure mathematics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/globalsip.2018.8646694","is_oa":false,"landing_page_url":"https://doi.org/10.1109/globalsip.2018.8646694","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 IEEE Global Conference on Signal and Information Processing (GlobalSIP)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G1252195697","display_name":null,"funder_award_id":"2029408","funder_id":"https://openalex.org/F4320334627","funder_display_name":"Engineering and Physical Sciences Research Council"}],"funders":[{"id":"https://openalex.org/F4320334627","display_name":"Engineering and Physical Sciences Research Council","ror":"https://ror.org/0439y7842"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":38,"referenced_works":["https://openalex.org/W28412257","https://openalex.org/W40442397","https://openalex.org/W273955616","https://openalex.org/W1534477342","https://openalex.org/W1605688901","https://openalex.org/W1966845080","https://openalex.org/W1978169406","https://openalex.org/W1983467829","https://openalex.org/W1986678274","https://openalex.org/W1988790447","https://openalex.org/W1993482030","https://openalex.org/W2000045479","https://openalex.org/W2013912476","https://openalex.org/W2021806873","https://openalex.org/W2023294425","https://openalex.org/W2024165284","https://openalex.org/W2079598971","https://openalex.org/W2093717447","https://openalex.org/W2100805904","https://openalex.org/W2101234009","https://openalex.org/W2112074816","https://openalex.org/W2118514362","https://openalex.org/W2119412403","https://openalex.org/W2262232454","https://openalex.org/W2309693750","https://openalex.org/W2327813014","https://openalex.org/W2516041031","https://openalex.org/W2617994470","https://openalex.org/W2801848240","https://openalex.org/W2903480912","https://openalex.org/W2912934387","https://openalex.org/W3004732066","https://openalex.org/W3121797243","https://openalex.org/W4212883601","https://openalex.org/W6601599523","https://openalex.org/W6610017368","https://openalex.org/W6632075054","https://openalex.org/W6675354045"],"related_works":["https://openalex.org/W2748952813","https://openalex.org/W2390279801","https://openalex.org/W2358668433","https://openalex.org/W2376932109","https://openalex.org/W2001405890","https://openalex.org/W2382290278","https://openalex.org/W2478288626","https://openalex.org/W2350741829","https://openalex.org/W2530322880","https://openalex.org/W1596801655"],"abstract_inverted_index":{"In":[0],"big":[1],"data":[2,14,35,60,92,102],"applications,":[3],"classical":[4,136],"ensemble":[5,30,54,109,137],"learning":[6,31,55,75,138],"is":[7,36,39,86,114,131],"typically":[8],"infeasible":[9],"on":[10],"the":[11,47,71,99,106,117,127,135],"raw":[12],"input":[13,59],"and":[15,104,130],"dimensionality":[16],"reduction":[17],"techniques":[18],"are":[19],"necessary.":[20],"To":[21],"this":[22],"end,":[23],"novel":[24],"framework":[25,85,113],"that":[26],"generalises":[27],"classic":[28],"flat-view":[29],"to":[32,51,79,88,95,126,133],"multidimensional":[33,91],"tensor-valued":[34],"introduced.":[37],"This":[38],"achieved":[40],"by":[41],"virtue":[42],"of":[43,73,98,108,119,140],"tensor":[44,53],"decompositions,":[45],"whereby":[46],"proposed":[48,112],"method,":[49],"referred":[50],"as":[52],"(TEL),":[56],"decomposes":[57],"every":[58],"sample":[61],"into":[62],"multiple":[63,74],"factors":[64],"which":[65],"allows":[66],"for":[67],"a":[68],"flexibility":[69],"in":[70,77,93],"choice":[72],"algorithms":[76],"order":[78,94],"improve":[80],"test":[81],"performance.":[82],"The":[83,111],"TEL":[84],"shown":[87,132],"naturally":[89],"compress":[90],"take":[96],"advantage":[97],"inherent":[100],"multi-way":[101],"structure":[103],"exploit":[105],"benefit":[107],"learning.":[110],"verified":[115],"through":[116],"application":[118],"Higher":[120],"Order":[121],"Singular":[122],"Value":[123],"Decomposition":[124],"(HOSVD)":[125],"ETH-80":[128],"dataset":[129],"outperform":[134],"approach":[139],"bootstrap":[141],"aggregating.":[142]},"counts_by_year":[{"year":2024,"cited_by_count":1},{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":1},{"year":2020,"cited_by_count":1},{"year":2018,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
