{"id":"https://openalex.org/W2550337300","doi":"https://doi.org/10.1109/ijcnn.2016.7727724","title":"Semi-supervised auto-encoder based on manifold learning","display_name":"Semi-supervised auto-encoder based on manifold learning","publication_year":2016,"publication_date":"2016-07-01","ids":{"openalex":"https://openalex.org/W2550337300","doi":"https://doi.org/10.1109/ijcnn.2016.7727724","mag":"2550337300"},"language":"en","primary_location":{"id":"doi:10.1109/ijcnn.2016.7727724","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn.2016.7727724","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2016 International Joint Conference on Neural Networks (IJCNN)","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://figshare.com/articles/conference_contribution/Semi-supervised_auto-encoder_based_on_manifold_learning/27394998","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5100377386","display_name":"Yawei Li","orcid":"https://orcid.org/0000-0002-8948-7892"},"institutions":[{"id":"https://openalex.org/I76569877","display_name":"Southeast University","ror":"https://ror.org/04ct4d772","country_code":"CN","type":"education","lineage":["https://openalex.org/I76569877"]},{"id":"https://openalex.org/I82951845","display_name":"RMIT University","ror":"https://ror.org/04ttjf776","country_code":"AU","type":"education","lineage":["https://openalex.org/I82951845"]}],"countries":["AU","CN"],"is_corresponding":false,"raw_author_name":"Yawei Li","raw_affiliation_strings":["RMIT University, Melbourne, Victoria, Australia","Southeast University, Nanjing, Jiangsu, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"RMIT University, Melbourne, Victoria, Australia","institution_ids":["https://openalex.org/I82951845"]},{"raw_affiliation_string":"Southeast University, Nanjing, Jiangsu, China","institution_ids":["https://openalex.org/I76569877"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101154079","display_name":"Lizuo Jin","orcid":null},"institutions":[{"id":"https://openalex.org/I76569877","display_name":"Southeast University","ror":"https://ror.org/04ct4d772","country_code":"CN","type":"education","lineage":["https://openalex.org/I76569877"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Lizuo Jin","raw_affiliation_strings":["Southeast University, Nanjing, Jiangsu, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Southeast University, Nanjing, Jiangsu, China","institution_ids":["https://openalex.org/I76569877"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5006614329","display_name":"A. K. Qin","orcid":"https://orcid.org/0000-0001-6631-1651"},"institutions":[{"id":"https://openalex.org/I82951845","display_name":"RMIT University","ror":"https://ror.org/04ttjf776","country_code":"AU","type":"education","lineage":["https://openalex.org/I82951845"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"A. K. Qin","raw_affiliation_strings":["RMIT University, Melbourne, Victoria, Australia"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"RMIT University, Melbourne, Victoria, Australia","institution_ids":["https://openalex.org/I82951845"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5019248683","display_name":"Changyin Sun","orcid":"https://orcid.org/0000-0001-9269-334X"},"institutions":[{"id":"https://openalex.org/I76569877","display_name":"Southeast University","ror":"https://ror.org/04ct4d772","country_code":"CN","type":"education","lineage":["https://openalex.org/I76569877"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Changyin Sun","raw_affiliation_strings":["Southeast University, Nanjing, Jiangsu, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Southeast University, Nanjing, Jiangsu, China","institution_ids":["https://openalex.org/I76569877"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5068243197","display_name":"Yew-Soon Ong","orcid":"https://orcid.org/0000-0002-4480-169X"},"institutions":[{"id":"https://openalex.org/I172675005","display_name":"Nanyang Technological University","ror":"https://ror.org/02e7b5302","country_code":"SG","type":"education","lineage":["https://openalex.org/I172675005"]}],"countries":["SG"],"is_corresponding":false,"raw_author_name":"Yew Soon Ong","raw_affiliation_strings":["Nanyang Technological University, Singapore"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Nanyang Technological University, Singapore","institution_ids":["https://openalex.org/I172675005"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5079642914","display_name":"Tong Cui","orcid":"https://orcid.org/0009-0006-5139-1242"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Tong Cui","raw_affiliation_strings":["The 28th Research Institute of CETC, Nanjing, Jiangsu, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"The 28th Research Institute of CETC, Nanjing, Jiangsu, China","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":3,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":"290","issue":null,"first_page":"4032","last_page":"4039"},"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":0.9987000226974487,"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":0.9987000226974487,"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/T11309","display_name":"Music and Audio Processing","score":0.9965000152587891,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"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/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.9959999918937683,"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":0.7354952692985535},{"id":"https://openalex.org/keywords/semi-supervised-learning","display_name":"Semi-supervised learning","score":0.6696763038635254},{"id":"https://openalex.org/keywords/encoder","display_name":"Encoder","score":0.6635607481002808},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6628186106681824},{"id":"https://openalex.org/keywords/autoencoder","display_name":"Autoencoder","score":0.6445505023002625},{"id":"https://openalex.org/keywords/feature-learning","display_name":"Feature learning","score":0.6065323352813721},{"id":"https://openalex.org/keywords/regularization","display_name":"Regularization (linguistics)","score":0.5996948480606079},{"id":"https://openalex.org/keywords/benchmarking","display_name":"Benchmarking","score":0.5993708372116089},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.5978779792785645},{"id":"https://openalex.org/keywords/labeled-data","display_name":"Labeled data","score":0.568354070186615},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5400787591934204},{"id":"https://openalex.org/keywords/encoding","display_name":"Encoding (memory)","score":0.5278734564781189},{"id":"https://openalex.org/keywords/unsupervised-learning","display_name":"Unsupervised learning","score":0.4918498992919922},{"id":"https://openalex.org/keywords/external-data-representation","display_name":"External Data Representation","score":0.49034953117370605},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.48886340856552124},{"id":"https://openalex.org/keywords/decoding-methods","display_name":"Decoding methods","score":0.4592798054218292},{"id":"https://openalex.org/keywords/generative-model","display_name":"Generative model","score":0.4569360315799713},{"id":"https://openalex.org/keywords/generative-grammar","display_name":"Generative grammar","score":0.3333086669445038},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.3085433542728424},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.12283709645271301}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7354952692985535},{"id":"https://openalex.org/C58973888","wikidata":"https://www.wikidata.org/wiki/Q1041418","display_name":"Semi-supervised learning","level":2,"score":0.6696763038635254},{"id":"https://openalex.org/C118505674","wikidata":"https://www.wikidata.org/wiki/Q42586063","display_name":"Encoder","level":2,"score":0.6635607481002808},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6628186106681824},{"id":"https://openalex.org/C101738243","wikidata":"https://www.wikidata.org/wiki/Q786435","display_name":"Autoencoder","level":3,"score":0.6445505023002625},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.6065323352813721},{"id":"https://openalex.org/C2776135515","wikidata":"https://www.wikidata.org/wiki/Q17143721","display_name":"Regularization (linguistics)","level":2,"score":0.5996948480606079},{"id":"https://openalex.org/C86251818","wikidata":"https://www.wikidata.org/wiki/Q816754","display_name":"Benchmarking","level":2,"score":0.5993708372116089},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.5978779792785645},{"id":"https://openalex.org/C2776145971","wikidata":"https://www.wikidata.org/wiki/Q30673951","display_name":"Labeled data","level":2,"score":0.568354070186615},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5400787591934204},{"id":"https://openalex.org/C125411270","wikidata":"https://www.wikidata.org/wiki/Q18653","display_name":"Encoding (memory)","level":2,"score":0.5278734564781189},{"id":"https://openalex.org/C8038995","wikidata":"https://www.wikidata.org/wiki/Q1152135","display_name":"Unsupervised learning","level":2,"score":0.4918498992919922},{"id":"https://openalex.org/C116409475","wikidata":"https://www.wikidata.org/wiki/Q1385056","display_name":"External Data Representation","level":2,"score":0.49034953117370605},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.48886340856552124},{"id":"https://openalex.org/C57273362","wikidata":"https://www.wikidata.org/wiki/Q576722","display_name":"Decoding methods","level":2,"score":0.4592798054218292},{"id":"https://openalex.org/C167966045","wikidata":"https://www.wikidata.org/wiki/Q5532625","display_name":"Generative model","level":3,"score":0.4569360315799713},{"id":"https://openalex.org/C39890363","wikidata":"https://www.wikidata.org/wiki/Q36108","display_name":"Generative grammar","level":2,"score":0.3333086669445038},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.3085433542728424},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.12283709645271301},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0},{"id":"https://openalex.org/C162853370","wikidata":"https://www.wikidata.org/wiki/Q39809","display_name":"Marketing","level":1,"score":0.0},{"id":"https://openalex.org/C144133560","wikidata":"https://www.wikidata.org/wiki/Q4830453","display_name":"Business","level":0,"score":0.0},{"id":"https://openalex.org/C94625758","wikidata":"https://www.wikidata.org/wiki/Q7163","display_name":"Politics","level":2,"score":0.0},{"id":"https://openalex.org/C17744445","wikidata":"https://www.wikidata.org/wiki/Q36442","display_name":"Political science","level":0,"score":0.0},{"id":"https://openalex.org/C199539241","wikidata":"https://www.wikidata.org/wiki/Q7748","display_name":"Law","level":1,"score":0.0}],"mesh":[],"locations_count":4,"locations":[{"id":"doi:10.1109/ijcnn.2016.7727724","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn.2016.7727724","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2016 International Joint Conference on Neural Networks (IJCNN)","raw_type":"proceedings-article"},{"id":"pmh:oai:alma.61RMIT_INST:11247200240001341","is_oa":false,"landing_page_url":"http://doi.org/10.1109/IJCNN.2016.7727724","pdf_url":null,"source":{"id":"https://openalex.org/S4306402074","display_name":"RMIT Research Repository (RMIT University Library)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I82951845","host_organization_name":"RMIT University","host_organization_lineage":["https://openalex.org/I82951845"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"text"},{"id":"pmh:oai:figshare.com:article/27394998","is_oa":true,"landing_page_url":"https://figshare.com/articles/conference_contribution/Semi-supervised_auto-encoder_based_on_manifold_learning/27394998","pdf_url":null,"source":{"id":"https://openalex.org/S4377196282","display_name":"Figshare","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I4210132348","host_organization_name":"Figshare (United Kingdom)","host_organization_lineage":["https://openalex.org/I4210132348"],"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":"Conference contribution"},{"id":"pmh:oai:researchbank.swinburne.edu.au:b3920310-a323-497c-8950-bd0bd929435b/1","is_oa":false,"landing_page_url":"http://hdl.handle.net/1959.3/438515","pdf_url":null,"source":{"id":"https://openalex.org/S4306401157","display_name":"Swinburne Research Bank (Swinburne University of Technology)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I57093077","host_organization_name":"Swinburne University of Technology","host_organization_lineage":["https://openalex.org/I57093077"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"2016 International Joint Conference on Neural Networks (IJCNN), Vancouver, BC, Canada, 24-29 July, 2016, pp. 4032-4039","raw_type":null}],"best_oa_location":{"id":"pmh:oai:figshare.com:article/27394998","is_oa":true,"landing_page_url":"https://figshare.com/articles/conference_contribution/Semi-supervised_auto-encoder_based_on_manifold_learning/27394998","pdf_url":null,"source":{"id":"https://openalex.org/S4377196282","display_name":"Figshare","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I4210132348","host_organization_name":"Figshare (United Kingdom)","host_organization_lineage":["https://openalex.org/I4210132348"],"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":"Conference contribution"},"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"},{"id":"https://openalex.org/F4320328198","display_name":"Vector Stiftung","ror":null}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":44,"referenced_works":["https://openalex.org/W189596042","https://openalex.org/W1546411676","https://openalex.org/W1587720067","https://openalex.org/W1811734137","https://openalex.org/W1838657242","https://openalex.org/W1965900320","https://openalex.org/W1984541135","https://openalex.org/W2001141328","https://openalex.org/W2023751370","https://openalex.org/W2025768430","https://openalex.org/W2050908788","https://openalex.org/W2072128103","https://openalex.org/W2074001187","https://openalex.org/W2095705004","https://openalex.org/W2097308346","https://openalex.org/W2100341991","https://openalex.org/W2100495367","https://openalex.org/W2104290444","https://openalex.org/W2110750681","https://openalex.org/W2110798204","https://openalex.org/W2116064496","https://openalex.org/W2118020555","https://openalex.org/W2118858186","https://openalex.org/W2138857742","https://openalex.org/W2145094598","https://openalex.org/W2145494108","https://openalex.org/W2152299995","https://openalex.org/W2154872931","https://openalex.org/W2159291644","https://openalex.org/W2163605009","https://openalex.org/W2163922914","https://openalex.org/W2166093887","https://openalex.org/W2172174689","https://openalex.org/W2188492526","https://openalex.org/W2218318129","https://openalex.org/W2249612659","https://openalex.org/W2293363371","https://openalex.org/W2407712691","https://openalex.org/W2614634292","https://openalex.org/W2997574889","https://openalex.org/W2997701990","https://openalex.org/W3007103623","https://openalex.org/W4231109964","https://openalex.org/W6691351710"],"related_works":["https://openalex.org/W3148060700","https://openalex.org/W34092691","https://openalex.org/W2365028544","https://openalex.org/W4309984931","https://openalex.org/W4282977123","https://openalex.org/W2949671220","https://openalex.org/W2531570999","https://openalex.org/W2013810580","https://openalex.org/W2794908468","https://openalex.org/W2096363773"],"abstract_inverted_index":{"Auto-encoder":[0],"is":[1,44],"a":[2,16,35,47,73,84],"popular":[3],"representation":[4,109,116],"learning":[5,38,55,117],"technique":[6],"which":[7,51,72],"can":[8],"capture":[9],"the":[10,61,100,105,126],"generative":[11],"model":[12],"of":[13,76,87,102,107,128],"data":[14,78],"via":[15],"encoding":[17],"and":[18,110],"decoding":[19],"procedure":[20],"typically":[21],"driven":[22],"by":[23],"reconstruction":[24],"errors":[25],"in":[26,71,81,122],"an":[27],"unsupervised":[28],"way.":[29],"In":[30],"this":[31],"paper,":[32],"we":[33],"propose":[34],"semi-supervised":[36,53,123],"manifold":[37,54],"based":[39,45,59],"auto-encoder":[40,49],"(named":[41],"semAE).":[42],"semAE":[43,103],"on":[46,60,93,119],"regularized":[48],"framework":[50],"leverages":[52],"to":[56,83,98,114],"impose":[57],"regularization":[58],"encoded":[62],"representation.":[63],"Our":[64],"proposed":[65],"approach":[66],"suits":[67],"more":[68],"practical":[69],"scenarios":[70],"small":[74],"number":[75,86],"labeled":[77],"are":[79,91],"available":[80],"addition":[82],"large":[85],"unlabeled":[88],"data.":[89],"Experiments":[90],"conducted":[92],"several":[94],"well-known":[95],"benchmarking":[96],"datasets":[97],"validate":[99],"efficacy":[101],"from":[104],"aspects":[106],"both":[108],"classification.":[111],"The":[112],"comparisons":[113],"state-of-the-art":[115],"methods":[118],"classification":[120],"performance":[121],"settings":[124],"demonstrate":[125],"superiority":[127],"our":[129],"approach.":[130]},"counts_by_year":[{"year":2021,"cited_by_count":1},{"year":2017,"cited_by_count":1}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}
