{"id":"https://openalex.org/W4365790226","doi":"https://doi.org/10.1109/tai.2023.3266190","title":"Neighboring Envelope Embedded Stacked Autoencoder for Deep Learning on Hierarchically Structured Samples","display_name":"Neighboring Envelope Embedded Stacked Autoencoder for Deep Learning on Hierarchically Structured Samples","publication_year":2023,"publication_date":"2023-04-13","ids":{"openalex":"https://openalex.org/W4365790226","doi":"https://doi.org/10.1109/tai.2023.3266190"},"language":"en","primary_location":{"id":"doi:10.1109/tai.2023.3266190","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tai.2023.3266190","pdf_url":null,"source":{"id":"https://openalex.org/S4210169448","display_name":"IEEE Transactions on Artificial Intelligence","issn_l":"2691-4581","issn":["2691-4581"],"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 Artificial Intelligence","raw_type":"journal-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/A5103168521","display_name":"Chuanyan Zhou","orcid":"https://orcid.org/0000-0003-0142-908X"},"institutions":[{"id":"https://openalex.org/I158842170","display_name":"Chongqing University","ror":"https://ror.org/023rhb549","country_code":"CN","type":"education","lineage":["https://openalex.org/I158842170"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Chuanyan Zhou","raw_affiliation_strings":["School of Microelectronics and Communication Engineering, Chongqing University, Chongqing, China"],"affiliations":[{"raw_affiliation_string":"School of Microelectronics and Communication Engineering, Chongqing University, Chongqing, China","institution_ids":["https://openalex.org/I158842170"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101676435","display_name":"Jie Ma","orcid":"https://orcid.org/0000-0003-1559-8255"},"institutions":[{"id":"https://openalex.org/I158842170","display_name":"Chongqing University","ror":"https://ror.org/023rhb549","country_code":"CN","type":"education","lineage":["https://openalex.org/I158842170"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jie Ma","raw_affiliation_strings":["School of Microelectronics and Communication Engineering, Chongqing University, Chongqing, China"],"affiliations":[{"raw_affiliation_string":"School of Microelectronics and Communication Engineering, Chongqing University, Chongqing, China","institution_ids":["https://openalex.org/I158842170"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100373563","display_name":"Fan Li","orcid":"https://orcid.org/0000-0002-2807-5720"},"institutions":[{"id":"https://openalex.org/I158842170","display_name":"Chongqing University","ror":"https://ror.org/023rhb549","country_code":"CN","type":"education","lineage":["https://openalex.org/I158842170"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Fan Li","raw_affiliation_strings":["School of Microelectronics and Communication Engineering, Chongqing University, Chongqing, China"],"affiliations":[{"raw_affiliation_string":"School of Microelectronics and Communication Engineering, Chongqing University, Chongqing, China","institution_ids":["https://openalex.org/I158842170"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5106920582","display_name":"Yongming Li","orcid":"https://orcid.org/0000-0002-7542-4356"},"institutions":[{"id":"https://openalex.org/I158842170","display_name":"Chongqing University","ror":"https://ror.org/023rhb549","country_code":"CN","type":"education","lineage":["https://openalex.org/I158842170"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yongming Li","raw_affiliation_strings":["School of Microelectronics and Communication Engineering, Chongqing University, Chongqing, China"],"affiliations":[{"raw_affiliation_string":"School of Microelectronics and Communication Engineering, Chongqing University, Chongqing, China","institution_ids":["https://openalex.org/I158842170"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5044924773","display_name":"Pin Wang","orcid":null},"institutions":[{"id":"https://openalex.org/I158842170","display_name":"Chongqing University","ror":"https://ror.org/023rhb549","country_code":"CN","type":"education","lineage":["https://openalex.org/I158842170"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Pin Wang","raw_affiliation_strings":["School of Microelectronics and Communication Engineering, Chongqing University, Chongqing, China"],"affiliations":[{"raw_affiliation_string":"School of Microelectronics and Communication Engineering, Chongqing University, Chongqing, China","institution_ids":["https://openalex.org/I158842170"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5103168521"],"corresponding_institution_ids":["https://openalex.org/I158842170"],"apc_list":null,"apc_paid":null,"fwci":0.1204,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.36969087,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":91,"max":95},"biblio":{"volume":"5","issue":"2","first_page":"737","last_page":"750"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10775","display_name":"Generative Adversarial Networks and Image Synthesis","score":0.98580002784729,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10775","display_name":"Generative Adversarial Networks and Image Synthesis","score":0.98580002784729,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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/T12535","display_name":"Machine Learning and Data Classification","score":0.97079998254776,"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/T10812","display_name":"Human Pose and Action Recognition","score":0.9599000215530396,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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/autoencoder","display_name":"Autoencoder","score":0.9442663788795471},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.8494057655334473},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7389427423477173},{"id":"https://openalex.org/keywords/sample","display_name":"Sample (material)","score":0.6813910603523254},{"id":"https://openalex.org/keywords/envelope","display_name":"Envelope (radar)","score":0.6559175848960876},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6356278657913208},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.6237807273864746},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.5668365955352783},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5635327696800232},{"id":"https://openalex.org/keywords/cluster-analysis","display_name":"Cluster analysis","score":0.4839203953742981},{"id":"https://openalex.org/keywords/function","display_name":"Function (biology)","score":0.42035210132598877},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3706190586090088},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3620942234992981},{"id":"https://openalex.org/keywords/physics","display_name":"Physics","score":0.07528373599052429}],"concepts":[{"id":"https://openalex.org/C101738243","wikidata":"https://www.wikidata.org/wiki/Q786435","display_name":"Autoencoder","level":3,"score":0.9442663788795471},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.8494057655334473},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7389427423477173},{"id":"https://openalex.org/C198531522","wikidata":"https://www.wikidata.org/wiki/Q485146","display_name":"Sample (material)","level":2,"score":0.6813910603523254},{"id":"https://openalex.org/C65155139","wikidata":"https://www.wikidata.org/wiki/Q5380912","display_name":"Envelope (radar)","level":3,"score":0.6559175848960876},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6356278657913208},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.6237807273864746},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.5668365955352783},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5635327696800232},{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.4839203953742981},{"id":"https://openalex.org/C14036430","wikidata":"https://www.wikidata.org/wiki/Q3736076","display_name":"Function (biology)","level":2,"score":0.42035210132598877},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3706190586090088},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3620942234992981},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.07528373599052429},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.0},{"id":"https://openalex.org/C97355855","wikidata":"https://www.wikidata.org/wiki/Q11473","display_name":"Thermodynamics","level":1,"score":0.0},{"id":"https://openalex.org/C554190296","wikidata":"https://www.wikidata.org/wiki/Q47528","display_name":"Radar","level":2,"score":0.0},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"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/C78458016","wikidata":"https://www.wikidata.org/wiki/Q840400","display_name":"Evolutionary biology","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tai.2023.3266190","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tai.2023.3266190","pdf_url":null,"source":{"id":"https://openalex.org/S4210169448","display_name":"IEEE Transactions on Artificial Intelligence","issn_l":"2691-4581","issn":["2691-4581"],"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 Artificial Intelligence","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G2153550768","display_name":null,"funder_award_id":"cstc2020jcyj-msxmX0100","funder_id":"https://openalex.org/F4320323172","funder_display_name":"Natural Science Foundation of Chongqing"},{"id":"https://openalex.org/G3136154376","display_name":null,"funder_award_id":"cstc2020jscx-fyzx0212","funder_id":"https://openalex.org/F4320323172","funder_display_name":"Natural Science Foundation of Chongqing"},{"id":"https://openalex.org/G4675203063","display_name":null,"funder_award_id":"cstc2020jcyj-msxmX0523","funder_id":"https://openalex.org/F4320323172","funder_display_name":"Natural Science Foundation of Chongqing"},{"id":"https://openalex.org/G5055499506","display_name":null,"funder_award_id":"cstc2020jscx-gksbx0009","funder_id":"https://openalex.org/F4320323172","funder_display_name":"Natural Science Foundation of Chongqing"},{"id":"https://openalex.org/G5282414445","display_name":null,"funder_award_id":"cstc2020jscx-msxm0369","funder_id":"https://openalex.org/F4320323172","funder_display_name":"Natural Science Foundation of Chongqing"},{"id":"https://openalex.org/G6948139269","display_name":null,"funder_award_id":"U21A20448","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G7292766450","display_name":null,"funder_award_id":"61771080","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320323172","display_name":"Natural Science Foundation of Chongqing","ror":"https://ror.org/01h0zpd94"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":50,"referenced_works":["https://openalex.org/W2064921494","https://openalex.org/W2103972604","https://openalex.org/W2105465844","https://openalex.org/W2118527002","https://openalex.org/W2141738323","https://openalex.org/W2141880113","https://openalex.org/W2150796457","https://openalex.org/W2153233077","https://openalex.org/W2165835468","https://openalex.org/W2176583664","https://openalex.org/W2529970009","https://openalex.org/W2735797020","https://openalex.org/W2746722496","https://openalex.org/W2753163043","https://openalex.org/W2789940649","https://openalex.org/W2807044098","https://openalex.org/W2891918059","https://openalex.org/W2911576848","https://openalex.org/W2913294167","https://openalex.org/W2921182884","https://openalex.org/W2922095228","https://openalex.org/W2923769473","https://openalex.org/W2938870765","https://openalex.org/W2962384338","https://openalex.org/W2968893289","https://openalex.org/W2978758977","https://openalex.org/W2985894713","https://openalex.org/W2991521245","https://openalex.org/W2997417149","https://openalex.org/W2998951762","https://openalex.org/W3002481680","https://openalex.org/W3006462480","https://openalex.org/W3010431126","https://openalex.org/W3013898566","https://openalex.org/W3015353599","https://openalex.org/W3022224028","https://openalex.org/W3034477267","https://openalex.org/W3035254752","https://openalex.org/W3081318531","https://openalex.org/W3093270589","https://openalex.org/W3101526497","https://openalex.org/W3102609171","https://openalex.org/W3103376008","https://openalex.org/W3111833549","https://openalex.org/W3114417845","https://openalex.org/W3126968387","https://openalex.org/W3190223470","https://openalex.org/W4224241910","https://openalex.org/W4309835919","https://openalex.org/W6683633756"],"related_works":["https://openalex.org/W2785535669","https://openalex.org/W4213225422","https://openalex.org/W3044458868","https://openalex.org/W4289653936","https://openalex.org/W2587789887","https://openalex.org/W4289860834","https://openalex.org/W4250304930","https://openalex.org/W4289656111","https://openalex.org/W2922457425","https://openalex.org/W2567271240"],"abstract_inverted_index":{"A":[0],"stacked":[1,89,146],"autoencoder":[2,90,147],"(SAE)":[3],"is":[4,60,67,98,143],"a":[5,174],"widely":[6],"used":[7],"deep":[8,12,78,171,200,213,229],"network.":[9],"However,":[10,55],"existing":[11,194],"SAEs":[13,37],"focus":[14],"on":[15,80,202],"original":[16,152,168],"samples":[17,130,136,153,166,205],"without":[18],"considering":[19],"the":[20,28,31,50,56,99,116,151,162,165,196],"hierarchical":[21,138],"structural":[22,139],"information":[23],"between":[24],"samples.":[25,83,113],"This":[26,84],"limits":[27],"accuracy":[29,51],"of":[30,93,134,164],"SAE.":[32],"In":[33,114],"recent":[34],"years,":[35],"state-of-the-art":[36],"have":[38],"suggested":[39],"improvements":[40],"in":[41,157,173],"network":[42,158],"structure,":[43,159],"cost":[44],"function,":[45],"and":[46,131,156,170,206,215],"parameter":[47],"optimization,":[48],"thereby":[49,160],"has":[52,184,221],"been":[53,222],"enhanced.":[54],"problem":[57],"mentioned":[58],"above":[59],"still":[61],"not":[62],"solved.":[63],"Therefore,":[64],"this":[65],"article":[66],"concerned":[68],"with":[69,137,167],"how":[70],"to":[71,149,210,227],"design":[72],"an":[73,144],"SAE":[74,208],"that":[75,106,181,220],"can":[76,224],"conduct":[77,211],"learning":[79,103,201],"hierarchically":[81,203],"structured":[82,204],"proposed":[85,197],"SAE\u2014neighboring":[86],"envelope":[87,102,135],"embedded":[88,145],"(NE_ESAE)\u2014mainly":[91],"consists":[92],"two":[94],"parts.":[95],"The":[96,141,177,218],"first":[97],"neighboring":[100,112],"sample":[101,108,120],"mechanism":[104],"(NSELM)":[105],"constructs":[107,118],"pairs":[109],"by":[110,122],"combining":[111],"addition,":[115],"NSELM":[117],"multilayer":[119,123],"spaces":[121],"iterative":[124],"mean":[125],"clustering,":[126],"which":[127],"considers":[128],"similar":[129],"generates":[132],"layers":[133],"information.":[140],"second":[142],"(ESAE)":[148],"consider":[150],"during":[154],"training":[155],"finding":[161],"relationship":[163],"features":[169,172],"better":[175,186],"manner.":[176],"experimental":[178],"results":[179],"show":[180],"our":[182],"method":[183],"significantly":[185],"performance":[187],"than":[188],"some":[189],"representative":[190],"methods.":[191],"Different":[192],"from":[193],"SAEs,":[195],"NE_ESAE":[198],"realizes":[199],"makes":[207],"able":[209],"cooperative":[212],"sampling":[214],"feature":[216],"learning.":[217],"advantage":[219],"gained":[223],"be":[225],"applied":[226],"other":[228],"neural":[230],"networks.":[231]},"counts_by_year":[{"year":2025,"cited_by_count":1}],"updated_date":"2026-03-27T05:58:40.876381","created_date":"2025-10-10T00:00:00"}