{"id":"https://openalex.org/W4416143069","doi":"https://doi.org/10.3103/s014641162570066x","title":"Graph Embedded Extreme Learning Machine Autoencoder with Multilayer Cyclic Structure","display_name":"Graph Embedded Extreme Learning Machine Autoencoder with Multilayer Cyclic Structure","publication_year":2025,"publication_date":"2025-08-01","ids":{"openalex":"https://openalex.org/W4416143069","doi":"https://doi.org/10.3103/s014641162570066x"},"language":"en","primary_location":{"id":"doi:10.3103/s014641162570066x","is_oa":false,"landing_page_url":"https://doi.org/10.3103/s014641162570066x","pdf_url":null,"source":{"id":"https://openalex.org/S17203304","display_name":"Automatic Control and Computer Sciences","issn_l":"0146-4116","issn":["0146-4116","1558-108X"],"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/P4310320267","host_organization_name":"Pleiades Publishing","host_organization_lineage":["https://openalex.org/P4310320267","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Pleiades Publishing","Springer Nature"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Automatic Control and Computer Sciences","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/A5100687937","display_name":"Di Wu","orcid":"https://orcid.org/0000-0002-2588-3001"},"institutions":[{"id":"https://openalex.org/I27688046","display_name":"Hunan Institute of Engineering","ror":"https://ror.org/03zj2rn70","country_code":"CN","type":"education","lineage":["https://openalex.org/I27688046"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Di Wu","raw_affiliation_strings":["College of Electrical and Information Engineering, Hunan Institute of Engineering, Xiangtan, China","Hunan Province Cooperative Innovation Center for Wind Power Equipment and Energy Conversion, Hunan Institute of Engineering, Xiangtan, China"],"affiliations":[{"raw_affiliation_string":"College of Electrical and Information Engineering, Hunan Institute of Engineering, Xiangtan, China","institution_ids":["https://openalex.org/I27688046"]},{"raw_affiliation_string":"Hunan Province Cooperative Innovation Center for Wind Power Equipment and Energy Conversion, Hunan Institute of Engineering, Xiangtan, China","institution_ids":["https://openalex.org/I27688046"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5033906902","display_name":"Z. J. Chen","orcid":null},"institutions":[{"id":"https://openalex.org/I27688046","display_name":"Hunan Institute of Engineering","ror":"https://ror.org/03zj2rn70","country_code":"CN","type":"education","lineage":["https://openalex.org/I27688046"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"ZiHan Chen","raw_affiliation_strings":["College of Electrical and Information Engineering, Hunan Institute of Engineering, Xiangtan, China"],"affiliations":[{"raw_affiliation_string":"College of Electrical and Information Engineering, Hunan Institute of Engineering, Xiangtan, China","institution_ids":["https://openalex.org/I27688046"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5100687937"],"corresponding_institution_ids":["https://openalex.org/I27688046"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.19129582,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"59","issue":"4","first_page":"516","last_page":"529"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12676","display_name":"Machine Learning and ELM","score":0.9940999746322632,"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"}},"topics":[{"id":"https://openalex.org/T12676","display_name":"Machine Learning and ELM","score":0.9940999746322632,"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/T10057","display_name":"Face and Expression Recognition","score":0.0008999999845400453,"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/T14413","display_name":"Advanced Technologies in Various Fields","score":0.00039999998989515007,"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/autoencoder","display_name":"Autoencoder","score":0.8504999876022339},{"id":"https://openalex.org/keywords/feature-learning","display_name":"Feature learning","score":0.6818000078201294},{"id":"https://openalex.org/keywords/dimensionality-reduction","display_name":"Dimensionality reduction","score":0.6797999739646912},{"id":"https://openalex.org/keywords/embedding","display_name":"Embedding","score":0.640500009059906},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.6255000233650208},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.6090999841690063},{"id":"https://openalex.org/keywords/graph-embedding","display_name":"Graph embedding","score":0.5953999757766724},{"id":"https://openalex.org/keywords/cluster-analysis","display_name":"Cluster analysis","score":0.595300018787384},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5936999917030334}],"concepts":[{"id":"https://openalex.org/C101738243","wikidata":"https://www.wikidata.org/wiki/Q786435","display_name":"Autoencoder","level":3,"score":0.8504999876022339},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7853000164031982},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.6818000078201294},{"id":"https://openalex.org/C70518039","wikidata":"https://www.wikidata.org/wiki/Q16000077","display_name":"Dimensionality reduction","level":2,"score":0.6797999739646912},{"id":"https://openalex.org/C41608201","wikidata":"https://www.wikidata.org/wiki/Q980509","display_name":"Embedding","level":2,"score":0.640500009059906},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.6255000233650208},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.6090999841690063},{"id":"https://openalex.org/C75564084","wikidata":"https://www.wikidata.org/wiki/Q5597085","display_name":"Graph embedding","level":3,"score":0.5953999757766724},{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.595300018787384},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5936999917030334},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.579200029373169},{"id":"https://openalex.org/C2780150128","wikidata":"https://www.wikidata.org/wiki/Q21948731","display_name":"Extreme learning machine","level":3,"score":0.5641999840736389},{"id":"https://openalex.org/C83665646","wikidata":"https://www.wikidata.org/wiki/Q42139305","display_name":"Feature vector","level":2,"score":0.5354999899864197},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.4959999918937683},{"id":"https://openalex.org/C111030470","wikidata":"https://www.wikidata.org/wiki/Q1430460","display_name":"Curse of dimensionality","level":2,"score":0.42989999055862427},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3666999936103821},{"id":"https://openalex.org/C103278499","wikidata":"https://www.wikidata.org/wiki/Q254465","display_name":"Similarity (geometry)","level":3,"score":0.36010000109672546},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.3199999928474426},{"id":"https://openalex.org/C62354387","wikidata":"https://www.wikidata.org/wiki/Q875399","display_name":"Boundary (topology)","level":2,"score":0.3124000132083893},{"id":"https://openalex.org/C151876577","wikidata":"https://www.wikidata.org/wiki/Q7049464","display_name":"Nonlinear dimensionality reduction","level":3,"score":0.304500013589859},{"id":"https://openalex.org/C116409475","wikidata":"https://www.wikidata.org/wiki/Q1385056","display_name":"External Data Representation","level":2,"score":0.29269999265670776},{"id":"https://openalex.org/C100595998","wikidata":"https://www.wikidata.org/wiki/Q11731931","display_name":"Graph kernel","level":5,"score":0.28679999709129333},{"id":"https://openalex.org/C14036430","wikidata":"https://www.wikidata.org/wiki/Q3736076","display_name":"Function (biology)","level":2,"score":0.2847999930381775},{"id":"https://openalex.org/C88230418","wikidata":"https://www.wikidata.org/wiki/Q131476","display_name":"Graph theory","level":2,"score":0.27090001106262207},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.2702000141143799},{"id":"https://openalex.org/C103275481","wikidata":"https://www.wikidata.org/wiki/Q6787889","display_name":"Matrix representation","level":3,"score":0.26840001344680786},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.26840001344680786},{"id":"https://openalex.org/C202286095","wikidata":"https://www.wikidata.org/wiki/Q579262","display_name":"Error function","level":2,"score":0.266400009393692},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.2655999958515167}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.3103/s014641162570066x","is_oa":false,"landing_page_url":"https://doi.org/10.3103/s014641162570066x","pdf_url":null,"source":{"id":"https://openalex.org/S17203304","display_name":"Automatic Control and Computer Sciences","issn_l":"0146-4116","issn":["0146-4116","1558-108X"],"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/P4310320267","host_organization_name":"Pleiades Publishing","host_organization_lineage":["https://openalex.org/P4310320267","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Pleiades Publishing","Springer Nature"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Automatic Control and Computer Sciences","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":21,"referenced_works":["https://openalex.org/W1969204685","https://openalex.org/W1987390907","https://openalex.org/W2026131661","https://openalex.org/W2032031606","https://openalex.org/W2042970394","https://openalex.org/W2065060269","https://openalex.org/W2100495367","https://openalex.org/W2111072639","https://openalex.org/W2141695047","https://openalex.org/W2301541953","https://openalex.org/W2393319904","https://openalex.org/W2561849210","https://openalex.org/W2624583263","https://openalex.org/W2798199013","https://openalex.org/W2969266941","https://openalex.org/W2990415293","https://openalex.org/W3115891954","https://openalex.org/W3123909522","https://openalex.org/W3196228261","https://openalex.org/W3203547808","https://openalex.org/W4200151020"],"related_works":[],"abstract_inverted_index":{"To":[0],"tackle":[1],"the":[2,13,35,38,55,60,73,78],"issues":[3],"of":[4,72],"incomplete":[5],"feature":[6,10,45,62,74,87,112],"reconstruction":[7,39],"and":[8,70,114],"insufficient":[9],"representation":[11,113],"in":[12],"traditional":[14],"extreme":[15],"learning":[16],"machine":[17],"autoencoder":[18],"(ELM-AE),":[19],"this":[20],"paper":[21],"proposes":[22],"a":[23,82],"multilayer":[24],"GEELM-AE":[25],"architecture":[26],"based":[27],"on":[28],"cyclic":[29],"structure":[30],"(GEELM-AE-MCS).":[31],"First,":[32],"we":[33,53,90],"embed":[34],"weights":[36],"into":[37,59],"error":[40],"function":[41],"to":[42,64,80,100,118],"enhance":[43,102],"local":[44],"clustering":[46],"by":[47],"integrating":[48],"graph":[49,56],"embedding":[50,57],"theory.":[51],"Second,":[52],"incorporate":[54],"matrix":[58],"ELM":[61],"space":[63],"preserve":[65],"both":[66],"global":[67],"structural":[68],"information":[69],"similarity":[71],"data,":[75],"thereby":[76],"enabling":[77],"algorithm":[79,103],"establish":[81],"more":[83],"effective":[84],"boundary":[85],"for":[86],"discrimination.":[88],"Finally,":[89],"propose":[91],"GEELM-AE-MCS,":[92],"which":[93],"leverages":[94],"each":[95],"self-encoder\u2019s":[96],"dimensionality":[97],"reduction":[98],"capability":[99],"further":[101],"performance.":[104],"Experimental":[105],"results":[106],"demonstrate":[107],"that":[108],"GEELM-AE-MCS":[109],"exhibits":[110],"superior":[111],"classification":[115],"capabilities":[116],"compared":[117],"state-of-the-art":[119],"algorithms.":[120]},"counts_by_year":[],"updated_date":"2026-03-07T16:01:11.037858","created_date":"2025-11-12T00:00:00"}
