{"id":"https://openalex.org/W4313038136","doi":"https://doi.org/10.1109/ijcnn55064.2022.9891921","title":"A Feature Fusion Analysis Model of Heterogeneous Data Based on Tensor Decomposition","display_name":"A Feature Fusion Analysis Model of Heterogeneous Data Based on Tensor Decomposition","publication_year":2022,"publication_date":"2022-07-18","ids":{"openalex":"https://openalex.org/W4313038136","doi":"https://doi.org/10.1109/ijcnn55064.2022.9891921"},"language":"en","primary_location":{"id":"doi:10.1109/ijcnn55064.2022.9891921","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn55064.2022.9891921","pdf_url":null,"source":{"id":"https://openalex.org/S4363607707","display_name":"2022 International Joint Conference on Neural Networks (IJCNN)","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":"2022 International Joint Conference on Neural Networks (IJCNN)","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/A5025317265","display_name":"Xianyang Chu","orcid":null},"institutions":[{"id":"https://openalex.org/I66867065","display_name":"East China Normal University","ror":"https://ror.org/02n96ep67","country_code":"CN","type":"education","lineage":["https://openalex.org/I66867065"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Xianyang Chu","raw_affiliation_strings":["School of Software Engineering, East China Normal University,Shanghai,China","School of Software Engineering, East China Normal University, Shanghai, China"],"affiliations":[{"raw_affiliation_string":"School of Software Engineering, East China Normal University,Shanghai,China","institution_ids":["https://openalex.org/I66867065"]},{"raw_affiliation_string":"School of Software Engineering, East China Normal University, Shanghai, China","institution_ids":["https://openalex.org/I66867065"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101923143","display_name":"Minghua Zhu","orcid":"https://orcid.org/0000-0002-6000-5837"},"institutions":[{"id":"https://openalex.org/I66867065","display_name":"East China Normal University","ror":"https://ror.org/02n96ep67","country_code":"CN","type":"education","lineage":["https://openalex.org/I66867065"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Minghua Zhu","raw_affiliation_strings":["School of Software Engineering, East China Normal University,Shanghai,China","School of Software Engineering, East China Normal University, Shanghai, China"],"affiliations":[{"raw_affiliation_string":"School of Software Engineering, East China Normal University,Shanghai,China","institution_ids":["https://openalex.org/I66867065"]},{"raw_affiliation_string":"School of Software Engineering, East China Normal University, Shanghai, China","institution_ids":["https://openalex.org/I66867065"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5084769547","display_name":"Hongyan Mao","orcid":"https://orcid.org/0000-0001-6749-1607"},"institutions":[{"id":"https://openalex.org/I66867065","display_name":"East China Normal University","ror":"https://ror.org/02n96ep67","country_code":"CN","type":"education","lineage":["https://openalex.org/I66867065"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Hongyan Mao","raw_affiliation_strings":["School of Software Engineering, East China Normal University,Shanghai,China","School of Software Engineering, East China Normal University, Shanghai, China"],"affiliations":[{"raw_affiliation_string":"School of Software Engineering, East China Normal University,Shanghai,China","institution_ids":["https://openalex.org/I66867065"]},{"raw_affiliation_string":"School of Software Engineering, East China Normal University, Shanghai, China","institution_ids":["https://openalex.org/I66867065"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5073476762","display_name":"Yunzhou Qiu","orcid":null},"institutions":[{"id":"https://openalex.org/I4210147322","display_name":"Shanghai Institute of Microsystem and Information Technology","ror":"https://ror.org/04nytyj38","country_code":"CN","type":"facility","lineage":["https://openalex.org/I19820366","https://openalex.org/I4210147322"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yunzhou Qiu","raw_affiliation_strings":["Shanghai Institute of Microsystem and Information Technology, Chinese Academy of Sciences,Shanghai,China","Shanghai Institute of Microsystem and Information Technology, Chinese Academy of Sciences, Shanghai, China"],"affiliations":[{"raw_affiliation_string":"Shanghai Institute of Microsystem and Information Technology, Chinese Academy of Sciences,Shanghai,China","institution_ids":["https://openalex.org/I4210147322"]},{"raw_affiliation_string":"Shanghai Institute of Microsystem and Information Technology, Chinese Academy of Sciences, Shanghai, China","institution_ids":["https://openalex.org/I4210147322"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5025317265"],"corresponding_institution_ids":["https://openalex.org/I66867065"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.20652174,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"2","issue":null,"first_page":"1","last_page":"7"},"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/T11269","display_name":"Algorithms and Data Compression","score":0.9218000173568726,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7145829200744629},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.5782281756401062},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.5080525875091553},{"id":"https://openalex.org/keywords/sensor-fusion","display_name":"Sensor fusion","score":0.4899405837059021},{"id":"https://openalex.org/keywords/tensor","display_name":"Tensor (intrinsic definition)","score":0.4502628445625305},{"id":"https://openalex.org/keywords/data-modeling","display_name":"Data modeling","score":0.4464591443538666},{"id":"https://openalex.org/keywords/matrix-decomposition","display_name":"Matrix decomposition","score":0.4429301619529724},{"id":"https://openalex.org/keywords/subspace-topology","display_name":"Subspace topology","score":0.43975088000297546},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.4367437958717346},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4361569285392761},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.414631724357605},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.15447252988815308},{"id":"https://openalex.org/keywords/database","display_name":"Database","score":0.10490521788597107}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7145829200744629},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.5782281756401062},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.5080525875091553},{"id":"https://openalex.org/C33954974","wikidata":"https://www.wikidata.org/wiki/Q486494","display_name":"Sensor fusion","level":2,"score":0.4899405837059021},{"id":"https://openalex.org/C155281189","wikidata":"https://www.wikidata.org/wiki/Q3518150","display_name":"Tensor (intrinsic definition)","level":2,"score":0.4502628445625305},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.4464591443538666},{"id":"https://openalex.org/C42355184","wikidata":"https://www.wikidata.org/wiki/Q1361088","display_name":"Matrix decomposition","level":3,"score":0.4429301619529724},{"id":"https://openalex.org/C32834561","wikidata":"https://www.wikidata.org/wiki/Q660730","display_name":"Subspace topology","level":2,"score":0.43975088000297546},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.4367437958717346},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4361569285392761},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.414631724357605},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.15447252988815308},{"id":"https://openalex.org/C77088390","wikidata":"https://www.wikidata.org/wiki/Q8513","display_name":"Database","level":1,"score":0.10490521788597107},{"id":"https://openalex.org/C158693339","wikidata":"https://www.wikidata.org/wiki/Q190524","display_name":"Eigenvalues and eigenvectors","level":2,"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/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","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},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ijcnn55064.2022.9891921","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn55064.2022.9891921","pdf_url":null,"source":{"id":"https://openalex.org/S4363607707","display_name":"2022 International Joint Conference on Neural Networks (IJCNN)","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":"2022 International Joint Conference on Neural Networks (IJCNN)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Industry, innovation and infrastructure","score":0.47999998927116394,"id":"https://metadata.un.org/sdg/9"}],"awards":[{"id":"https://openalex.org/G8497710839","display_name":null,"funder_award_id":"2020YFB2104202","funder_id":"https://openalex.org/F4320335777","funder_display_name":"National Key Research and Development Program of China"}],"funders":[{"id":"https://openalex.org/F4320335777","display_name":"National Key Research and Development Program of China","ror":null}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":27,"referenced_works":["https://openalex.org/W2024165284","https://openalex.org/W2098923380","https://openalex.org/W2184188583","https://openalex.org/W2431890537","https://openalex.org/W2731053425","https://openalex.org/W2811204324","https://openalex.org/W2899639140","https://openalex.org/W2911733517","https://openalex.org/W2923380536","https://openalex.org/W2954228910","https://openalex.org/W2961963176","https://openalex.org/W2963615402","https://openalex.org/W3013022628","https://openalex.org/W3015622603","https://openalex.org/W3042029390","https://openalex.org/W3048631361","https://openalex.org/W3090167799","https://openalex.org/W3102692100","https://openalex.org/W3126937085","https://openalex.org/W3134909912","https://openalex.org/W3159203577","https://openalex.org/W3190219082","https://openalex.org/W4200107606","https://openalex.org/W6675190910","https://openalex.org/W6686207219","https://openalex.org/W6717575008","https://openalex.org/W6766192726"],"related_works":["https://openalex.org/W1980381208","https://openalex.org/W2364594919","https://openalex.org/W2167092671","https://openalex.org/W1861706286","https://openalex.org/W2219338811","https://openalex.org/W2149583853","https://openalex.org/W2143002539","https://openalex.org/W4293472652","https://openalex.org/W2130386332","https://openalex.org/W2890370013"],"abstract_inverted_index":{"The":[0,21],"vast":[1],"volume":[2],"of":[3,18,23,27,32,56,61,73,111,157,165,188],"heterogeneous":[4,39,62,74,94,178],"multi-source":[5],"data":[6,34,43,75,95,127,171],"generated":[7],"by":[8,185],"terminal":[9],"devices":[10],"is":[11,37],"expanding":[12],"exponentially":[13],"as":[14],"the":[15,30,82,118,136,147],"Intelligent":[16],"Internet":[17,26],"Things":[19,28],"develops.":[20],"construction":[22],"an":[24,163],"intelligent":[25],"requires":[29],"extraction":[31,45,110],"crucial":[33],"features,":[35],"which":[36],"from":[38,93,129],"multi-source.":[40],"However,":[41],"traditional":[42],"feature":[44,69,109],"approaches":[46],"are":[47,54,140],"limited":[48],"to":[49,87,122],"single-source,":[50],"vector":[51],"space":[52,113],"and":[53,115,124],"incapable":[55],"capturing":[57],"high-dimensional":[58],"nonlinear":[59],"correlations":[60],"data.":[63],"This":[64],"paper":[65],"innovatively":[66],"proposes":[67],"a":[68,99,169,186],"fusion":[70,138,179],"analysis":[71,158,173],"model":[72,161],"based":[76,103],"on":[77,104,146],"tensor":[78,83,100],"decomposition.":[79],"We":[80,97],"optimize":[81],"ring":[84],"decomposition":[85],"method":[86],"initially":[88],"extract":[89],"low-dimensional":[90],"latent":[91],"features":[92,128,139],"effectively.":[96],"construct":[98],"autoencoder":[101],"network":[102],"subspace":[105],"mapping":[106],"for":[107],"core":[108,126],"subtensor":[112,131],"data,":[114],"then":[116],"use":[117],"Kronecker":[119],"product":[120],"operation":[121],"associate":[123],"fuse":[125],"different":[130],"spaces.":[132],"After":[133],"multiple":[134],"fusions,":[135],"final":[137],"obtained.":[141],"Experiments":[142],"were":[143],"carried":[144],"out":[145],"multi-modal":[148],"dataset":[149],"CUAVE.":[150],"Experimental":[151],"results":[152],"show":[153],"that,":[154],"in":[155],"terms":[156],"accuracy,":[159],"this":[160],"has":[162],"improvement":[164],"18%":[166],"compared":[167,175],"with":[168,176],"single":[170],"source":[172],"model;":[174],"other":[177],"models,":[180],"it":[181],"can":[182],"be":[183],"increased":[184],"maximum":[187],"34%.":[189]},"counts_by_year":[],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
