{"id":"https://openalex.org/W2945467354","doi":"https://doi.org/10.1109/tnnls.2020.2984831","title":"Multilinear Compressive Learning","display_name":"Multilinear Compressive Learning","publication_year":2020,"publication_date":"2020-04-17","ids":{"openalex":"https://openalex.org/W2945467354","doi":"https://doi.org/10.1109/tnnls.2020.2984831","mag":"2945467354","pmid":"https://pubmed.ncbi.nlm.nih.gov/32310801"},"language":"en","primary_location":{"id":"doi:10.1109/tnnls.2020.2984831","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tnnls.2020.2984831","pdf_url":null,"source":{"id":"https://openalex.org/S4210175523","display_name":"IEEE Transactions on Neural Networks and Learning Systems","issn_l":"2162-237X","issn":["2162-237X","2162-2388"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["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 Neural Networks and Learning Systems","raw_type":"journal-article"},"type":"article","indexed_in":["arxiv","crossref","pubmed"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/1905.07481","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5013499653","display_name":"Dat Thanh Tran","orcid":"https://orcid.org/0000-0002-5922-3458"},"institutions":[{"id":"https://openalex.org/I166825849","display_name":"Tampere University","ror":"https://ror.org/033003e23","country_code":"FI","type":"education","lineage":["https://openalex.org/I166825849"]}],"countries":["FI"],"is_corresponding":true,"raw_author_name":"Dat Thanh Tran","raw_affiliation_strings":["Tampere University, Tampere, Finland"],"affiliations":[{"raw_affiliation_string":"Tampere University, Tampere, Finland","institution_ids":["https://openalex.org/I166825849"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5046067674","display_name":"Mehmet Yama\u00e7","orcid":"https://orcid.org/0000-0002-1681-6931"},"institutions":[{"id":"https://openalex.org/I166825849","display_name":"Tampere University","ror":"https://ror.org/033003e23","country_code":"FI","type":"education","lineage":["https://openalex.org/I166825849"]}],"countries":["FI"],"is_corresponding":false,"raw_author_name":"Mehmet Yamac","raw_affiliation_strings":["Tampere University, Tampere, Finland"],"affiliations":[{"raw_affiliation_string":"Tampere University, Tampere, Finland","institution_ids":["https://openalex.org/I166825849"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5057088639","display_name":"Aysen Degerli","orcid":"https://orcid.org/0000-0002-9478-033X"},"institutions":[{"id":"https://openalex.org/I166825849","display_name":"Tampere University","ror":"https://ror.org/033003e23","country_code":"FI","type":"education","lineage":["https://openalex.org/I166825849"]}],"countries":["FI"],"is_corresponding":false,"raw_author_name":"Aysen Degerli","raw_affiliation_strings":["Tampere University, Tampere, Finland"],"affiliations":[{"raw_affiliation_string":"Tampere University, Tampere, Finland","institution_ids":["https://openalex.org/I166825849"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5007583477","display_name":"Moncef Gabbouj","orcid":"https://orcid.org/0000-0002-9788-2323"},"institutions":[{"id":"https://openalex.org/I166825849","display_name":"Tampere University","ror":"https://ror.org/033003e23","country_code":"FI","type":"education","lineage":["https://openalex.org/I166825849"]}],"countries":["FI"],"is_corresponding":false,"raw_author_name":"Moncef Gabbouj","raw_affiliation_strings":["Tampere University, Tampere, Finland"],"affiliations":[{"raw_affiliation_string":"Tampere University, Tampere, Finland","institution_ids":["https://openalex.org/I166825849"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5064535836","display_name":"Alexandros Iosifidis","orcid":"https://orcid.org/0000-0003-4807-1345"},"institutions":[{"id":"https://openalex.org/I204337017","display_name":"Aarhus University","ror":"https://ror.org/01aj84f44","country_code":"DK","type":"education","lineage":["https://openalex.org/I204337017"]}],"countries":["DK"],"is_corresponding":false,"raw_author_name":"Alexandros Iosifidis","raw_affiliation_strings":["Aarhus University, Aarhus, Denmark"],"affiliations":[{"raw_affiliation_string":"Aarhus University, Aarhus, Denmark","institution_ids":["https://openalex.org/I204337017"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5013499653"],"corresponding_institution_ids":["https://openalex.org/I166825849"],"apc_list":null,"apc_paid":null,"fwci":3.4083,"has_fulltext":true,"cited_by_count":30,"citation_normalized_percentile":{"value":0.92962295,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":94,"max":98},"biblio":{"volume":"32","issue":"4","first_page":"1512","last_page":"1524"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10500","display_name":"Sparse and Compressive Sensing Techniques","score":1.0,"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"}},"topics":[{"id":"https://openalex.org/T10500","display_name":"Sparse and Compressive Sensing Techniques","score":1.0,"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/T10326","display_name":"Indoor and Outdoor Localization Technologies","score":0.998199999332428,"subfield":{"id":"https://openalex.org/subfields/2208","display_name":"Electrical and Electronic Engineering"},"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/T11739","display_name":"Microwave Imaging and Scattering Analysis","score":0.9955999851226807,"subfield":{"id":"https://openalex.org/subfields/2204","display_name":"Biomedical Engineering"},"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/compressed-sensing","display_name":"Compressed sensing","score":0.8472256660461426},{"id":"https://openalex.org/keywords/multilinear-map","display_name":"Multilinear map","score":0.7619718313217163},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6713575720787048},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.556602954864502},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.503438413143158},{"id":"https://openalex.org/keywords/projection","display_name":"Projection (relational algebra)","score":0.4931032359600067},{"id":"https://openalex.org/keywords/tensor","display_name":"Tensor (intrinsic definition)","score":0.48424383997917175},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.4831940233707428},{"id":"https://openalex.org/keywords/dimension","display_name":"Dimension (graph theory)","score":0.4629074037075043},{"id":"https://openalex.org/keywords/signal","display_name":"SIGNAL (programming language)","score":0.4507963955402374},{"id":"https://openalex.org/keywords/random-projection","display_name":"Random projection","score":0.4362155497074127},{"id":"https://openalex.org/keywords/signal-reconstruction","display_name":"Signal reconstruction","score":0.41508013010025024},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.35291558504104614},{"id":"https://openalex.org/keywords/signal-processing","display_name":"Signal processing","score":0.34678125381469727},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.2716362774372101},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.18981629610061646},{"id":"https://openalex.org/keywords/digital-signal-processing","display_name":"Digital signal processing","score":0.160739928483963}],"concepts":[{"id":"https://openalex.org/C124851039","wikidata":"https://www.wikidata.org/wiki/Q2665459","display_name":"Compressed sensing","level":2,"score":0.8472256660461426},{"id":"https://openalex.org/C84392682","wikidata":"https://www.wikidata.org/wiki/Q1952404","display_name":"Multilinear map","level":2,"score":0.7619718313217163},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6713575720787048},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.556602954864502},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.503438413143158},{"id":"https://openalex.org/C57493831","wikidata":"https://www.wikidata.org/wiki/Q3134666","display_name":"Projection (relational algebra)","level":2,"score":0.4931032359600067},{"id":"https://openalex.org/C155281189","wikidata":"https://www.wikidata.org/wiki/Q3518150","display_name":"Tensor (intrinsic definition)","level":2,"score":0.48424383997917175},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.4831940233707428},{"id":"https://openalex.org/C33676613","wikidata":"https://www.wikidata.org/wiki/Q13415176","display_name":"Dimension (graph theory)","level":2,"score":0.4629074037075043},{"id":"https://openalex.org/C2779843651","wikidata":"https://www.wikidata.org/wiki/Q7390335","display_name":"SIGNAL (programming language)","level":2,"score":0.4507963955402374},{"id":"https://openalex.org/C2777036070","wikidata":"https://www.wikidata.org/wiki/Q18393452","display_name":"Random projection","level":2,"score":0.4362155497074127},{"id":"https://openalex.org/C70958404","wikidata":"https://www.wikidata.org/wiki/Q7512728","display_name":"Signal reconstruction","level":4,"score":0.41508013010025024},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.35291558504104614},{"id":"https://openalex.org/C104267543","wikidata":"https://www.wikidata.org/wiki/Q208163","display_name":"Signal processing","level":3,"score":0.34678125381469727},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.2716362774372101},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.18981629610061646},{"id":"https://openalex.org/C84462506","wikidata":"https://www.wikidata.org/wiki/Q173142","display_name":"Digital signal processing","level":2,"score":0.160739928483963},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","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/C9390403","wikidata":"https://www.wikidata.org/wiki/Q3966","display_name":"Computer hardware","level":1,"score":0.0}],"mesh":[],"locations_count":4,"locations":[{"id":"doi:10.1109/tnnls.2020.2984831","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tnnls.2020.2984831","pdf_url":null,"source":{"id":"https://openalex.org/S4210175523","display_name":"IEEE Transactions on Neural Networks and Learning Systems","issn_l":"2162-237X","issn":["2162-237X","2162-2388"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["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 Neural Networks and Learning Systems","raw_type":"journal-article"},{"id":"pmid:32310801","is_oa":false,"landing_page_url":"https://pubmed.ncbi.nlm.nih.gov/32310801","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":"IEEE transactions on neural networks and learning systems","raw_type":null},{"id":"pmh:oai:arXiv.org:1905.07481","is_oa":true,"landing_page_url":"http://arxiv.org/abs/1905.07481","pdf_url":"https://arxiv.org/pdf/1905.07481","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"},{"id":"pmh:oai:pure.atira.dk:publications/2a6ff458-0764-40c1-9246-13857668ecb8","is_oa":true,"landing_page_url":"https://pure.au.dk/portal/en/publications/2a6ff458-0764-40c1-9246-13857668ecb8","pdf_url":null,"source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Tran, D T, Yamac, M, Degerli, A, Gabbouj, M & Iosifidis, A 2021, 'Multilinear compressive learning', IEEE Transactions on Neural Networks and Learning Systems, vol. 32, no. 4, 9070152, pp. 1512-1524. https://doi.org/10.1109/TNNLS.2020.2984831","raw_type":"info:eu-repo/semantics/publishedVersion"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:1905.07481","is_oa":true,"landing_page_url":"http://arxiv.org/abs/1905.07481","pdf_url":"https://arxiv.org/pdf/1905.07481","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/F4320320300","display_name":"European Commission","ror":"https://ror.org/00k4n6c32"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":80,"referenced_works":["https://openalex.org/W49093376","https://openalex.org/W119906723","https://openalex.org/W1522301498","https://openalex.org/W1533861849","https://openalex.org/W1677182931","https://openalex.org/W1765998458","https://openalex.org/W1828462930","https://openalex.org/W1834627138","https://openalex.org/W1956120499","https://openalex.org/W1964811212","https://openalex.org/W1967077133","https://openalex.org/W1967448999","https://openalex.org/W1967696752","https://openalex.org/W1978680118","https://openalex.org/W1986736933","https://openalex.org/W1993069924","https://openalex.org/W1996901117","https://openalex.org/W1997038974","https://openalex.org/W2001380973","https://openalex.org/W2002317295","https://openalex.org/W2013912476","https://openalex.org/W2061354873","https://openalex.org/W2066792693","https://openalex.org/W2078204800","https://openalex.org/W2100452521","https://openalex.org/W2116148865","https://openalex.org/W2117852776","https://openalex.org/W2118297240","https://openalex.org/W2119412403","https://openalex.org/W2120961178","https://openalex.org/W2122207860","https://openalex.org/W2123045220","https://openalex.org/W2126131432","https://openalex.org/W2127271355","https://openalex.org/W2131524184","https://openalex.org/W2132639093","https://openalex.org/W2141076176","https://openalex.org/W2154332973","https://openalex.org/W2160547390","https://openalex.org/W2164452299","https://openalex.org/W2167215970","https://openalex.org/W2296278149","https://openalex.org/W2296616510","https://openalex.org/W2302255633","https://openalex.org/W2517723848","https://openalex.org/W2548198050","https://openalex.org/W2701059868","https://openalex.org/W2753090725","https://openalex.org/W2758217051","https://openalex.org/W2766136689","https://openalex.org/W2768347741","https://openalex.org/W2792310407","https://openalex.org/W2809871795","https://openalex.org/W2897900009","https://openalex.org/W2911737132","https://openalex.org/W2950248853","https://openalex.org/W2950967261","https://openalex.org/W2963048316","https://openalex.org/W2963122061","https://openalex.org/W2963341393","https://openalex.org/W2963704562","https://openalex.org/W2969219076","https://openalex.org/W3105291792","https://openalex.org/W3118608800","https://openalex.org/W3158349976","https://openalex.org/W4212940801","https://openalex.org/W4238209155","https://openalex.org/W4239096300","https://openalex.org/W4250955649","https://openalex.org/W4252265035","https://openalex.org/W6604859597","https://openalex.org/W6631190155","https://openalex.org/W6631943919","https://openalex.org/W6679667936","https://openalex.org/W6684563725","https://openalex.org/W6698183232","https://openalex.org/W6729487588","https://openalex.org/W6741042017","https://openalex.org/W6746138031","https://openalex.org/W6787972765"],"related_works":["https://openalex.org/W2378166785","https://openalex.org/W2156466545","https://openalex.org/W2922481674","https://openalex.org/W2379468505","https://openalex.org/W3177385724","https://openalex.org/W1572092093","https://openalex.org/W3104324607","https://openalex.org/W2893527900","https://openalex.org/W2535617683","https://openalex.org/W2537887767"],"abstract_inverted_index":{"Compressive":[0],"learning":[1,17],"(CL)":[2],"is":[3,142],"an":[4],"emerging":[5],"topic":[6],"that":[7,108,138,163],"combines":[8],"signal":[9,85,88],"acquisition":[10,120],"via":[11],"compressive":[12],"sensing":[13],"(CS)":[14],"and":[15,52,61,122,153,174,178],"machine":[16],"to":[18,86,146],"perform":[19,87],"inference":[20,126],"tasks":[21],"directly":[22],"on":[23,82,128],"a":[24,34,106],"small":[25],"number":[26],"of":[27,95,115,184],"measurements.":[28,132],"Many":[29],"data":[30],"modalities":[31],"naturally":[32],"have":[33],"multidimensional":[35,93,116,195],"or":[36,42,58,78],"tensorial":[37,113],"format,":[38],"with":[39],"each":[40],"dimension":[41],"tensor":[43],"mode":[44],"representing":[45],"different":[46],"features":[47],"such":[48],"as":[49],"the":[50,59,72,83,92,96,112,119,124,129,139,168,182,185],"spatial":[51,60],"temporal":[53],"information":[54,63],"in":[55,64,68,118,150,171],"video":[56],"sequences":[57],"spectral":[62],"hyperspectral":[65],"images.":[66],"However,":[67],"existing":[69],"CL":[70,104],"frameworks,":[71],"CS":[73],"component":[74],"utilizes":[75],"either":[76],"random":[77],"learned":[79],"linear":[80],"projection":[81],"vectorized":[84],"acquisition,":[89],"thus":[90],"discarding":[91],"structure":[94],"signals.":[97,196],"In":[98],"this":[99],"article,":[100],"we":[101,159],"propose":[102],"multilinear":[103],"(MCL),":[105],"framework":[107,141,166,170],"takes":[109],"into":[110],"account":[111],"nature":[114],"signals":[117,187],"step":[121],"builds":[123],"subsequent":[125],"model":[127],"structurally":[130],"sensed":[131],"Our":[133],"theoretical":[134],"complexity":[135],"analysis":[136],"shows":[137],"proposed":[140],"more":[143],"efficient":[144,192],"compared":[145],"its":[147],"vector-based":[148,169],"counterpart":[149],"both":[151],"memory":[152],"computation":[154],"requirement.":[155],"With":[156],"extensive":[157],"experiments,":[158],"also":[160],"empirically":[161],"show":[162],"our":[164],"MCL":[165],"outperforms":[167],"object":[172],"classification":[173],"face":[175],"recognition":[176],"tasks,":[177],"scales":[179],"favorably":[180],"when":[181],"dimensionalities":[183],"original":[186],"increase,":[188],"making":[189],"it":[190],"highly":[191],"for":[193],"high-dimensional":[194]},"counts_by_year":[{"year":2025,"cited_by_count":5},{"year":2024,"cited_by_count":6},{"year":2023,"cited_by_count":4},{"year":2022,"cited_by_count":6},{"year":2021,"cited_by_count":7},{"year":2020,"cited_by_count":2}],"updated_date":"2026-03-20T23:20:44.827607","created_date":"2025-10-10T00:00:00"}