{"id":"https://openalex.org/W2773643226","doi":"https://doi.org/10.1109/smc.2017.8122732","title":"Autoencoder, low rank approximation and pseudoinverse learning algorithm","display_name":"Autoencoder, low rank approximation and pseudoinverse learning algorithm","publication_year":2017,"publication_date":"2017-10-01","ids":{"openalex":"https://openalex.org/W2773643226","doi":"https://doi.org/10.1109/smc.2017.8122732","mag":"2773643226"},"language":"en","primary_location":{"id":"doi:10.1109/smc.2017.8122732","is_oa":false,"landing_page_url":"https://doi.org/10.1109/smc.2017.8122732","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 IEEE International Conference on Systems, Man, and Cybernetics (SMC)","raw_type":"proceedings-article"},"type":"conference-paper","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/A5100614211","display_name":"Ke Wang","orcid":"https://orcid.org/0000-0002-4808-2637"},"institutions":[{"id":"https://openalex.org/I125839683","display_name":"Beijing Institute of Technology","ror":"https://ror.org/01skt4w74","country_code":"CN","type":"education","lineage":["https://openalex.org/I125839683","https://openalex.org/I890469752"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Ke Wang","raw_affiliation_strings":["School of Computer Science and Technology, Beijing Institute of Technology, Beijing, P. R. China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Computer Science and Technology, Beijing Institute of Technology, Beijing, P. R. China","institution_ids":["https://openalex.org/I125839683"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5020083340","display_name":"Ping Guo","orcid":"https://orcid.org/0000-0002-7122-1084"},"institutions":[{"id":"https://openalex.org/I125839683","display_name":"Beijing Institute of Technology","ror":"https://ror.org/01skt4w74","country_code":"CN","type":"education","lineage":["https://openalex.org/I125839683","https://openalex.org/I890469752"]},{"id":"https://openalex.org/I25254941","display_name":"Beijing Normal University","ror":"https://ror.org/022k4wk35","country_code":"CN","type":"education","lineage":["https://openalex.org/I25254941"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Ping Guo","raw_affiliation_strings":["Image Processing and Pattern Recognition Laboratory, Beijing Normal University, Beijing, P. R. China","School of Computer Science and Technology, Beijing Institute of Technology, Beijing, P. R. China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Image Processing and Pattern Recognition Laboratory, Beijing Normal University, Beijing, P. R. China","institution_ids":["https://openalex.org/I25254941"]},{"raw_affiliation_string":"School of Computer Science and Technology, Beijing Institute of Technology, Beijing, P. R. China","institution_ids":["https://openalex.org/I125839683"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100393241","display_name":"Xin Xin","orcid":"https://orcid.org/0000-0001-6116-9115"},"institutions":[{"id":"https://openalex.org/I125839683","display_name":"Beijing Institute of Technology","ror":"https://ror.org/01skt4w74","country_code":"CN","type":"education","lineage":["https://openalex.org/I125839683","https://openalex.org/I890469752"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xin Xin","raw_affiliation_strings":["School of Computer Science and Technology, Beijing Institute of Technology, Beijing, P. R. China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Computer Science and Technology, Beijing Institute of Technology, Beijing, P. R. China","institution_ids":["https://openalex.org/I125839683"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5015740411","display_name":"Zebin Ye","orcid":null},"institutions":[{"id":"https://openalex.org/I125839683","display_name":"Beijing Institute of Technology","ror":"https://ror.org/01skt4w74","country_code":"CN","type":"education","lineage":["https://openalex.org/I125839683","https://openalex.org/I890469752"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zebin Ye","raw_affiliation_strings":["School of Computer Science and Technology, Beijing Institute of Technology, Beijing, P. R. China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Computer Science and Technology, Beijing Institute of Technology, Beijing, P. R. China","institution_ids":["https://openalex.org/I125839683"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":2.4916,"has_fulltext":false,"cited_by_count":41,"citation_normalized_percentile":{"value":0.92824146,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":90,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"948","last_page":"953"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10320","display_name":"Neural Networks and Applications","score":0.9991000294685364,"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/T10320","display_name":"Neural Networks and Applications","score":0.9991000294685364,"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/T12676","display_name":"Machine Learning and ELM","score":0.9986000061035156,"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":0.9951000213623047,"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/moore\u2013penrose-pseudoinverse","display_name":"Moore\u2013Penrose pseudoinverse","score":0.8531291484832764},{"id":"https://openalex.org/keywords/autoencoder","display_name":"Autoencoder","score":0.8118394017219543},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7234548330307007},{"id":"https://openalex.org/keywords/gradient-descent","display_name":"Gradient descent","score":0.6131815910339355},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6097157001495361},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.5949957966804504},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.5777115225791931},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.5581545829772949},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.4410804510116577},{"id":"https://openalex.org/keywords/rank","display_name":"Rank (graph theory)","score":0.4395410716533661},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3864448070526123},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.34414634108543396},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.1465364396572113},{"id":"https://openalex.org/keywords/inverse","display_name":"Inverse","score":0.11333942413330078}],"concepts":[{"id":"https://openalex.org/C21556879","wikidata":"https://www.wikidata.org/wiki/Q43219517","display_name":"Moore\u2013Penrose pseudoinverse","level":3,"score":0.8531291484832764},{"id":"https://openalex.org/C101738243","wikidata":"https://www.wikidata.org/wiki/Q786435","display_name":"Autoencoder","level":3,"score":0.8118394017219543},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7234548330307007},{"id":"https://openalex.org/C153258448","wikidata":"https://www.wikidata.org/wiki/Q1199743","display_name":"Gradient descent","level":3,"score":0.6131815910339355},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6097157001495361},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.5949957966804504},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5777115225791931},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5581545829772949},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.4410804510116577},{"id":"https://openalex.org/C164226766","wikidata":"https://www.wikidata.org/wiki/Q7293202","display_name":"Rank (graph theory)","level":2,"score":0.4395410716533661},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3864448070526123},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.34414634108543396},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.1465364396572113},{"id":"https://openalex.org/C207467116","wikidata":"https://www.wikidata.org/wiki/Q4385666","display_name":"Inverse","level":2,"score":0.11333942413330078},{"id":"https://openalex.org/C114614502","wikidata":"https://www.wikidata.org/wiki/Q76592","display_name":"Combinatorics","level":1,"score":0.0},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/smc.2017.8122732","is_oa":false,"landing_page_url":"https://doi.org/10.1109/smc.2017.8122732","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 IEEE International Conference on Systems, Man, and Cybernetics (SMC)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320335569","display_name":"Joint Fund of Astronomy","ror":null}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":12,"referenced_works":["https://openalex.org/W653924242","https://openalex.org/W1988115241","https://openalex.org/W2025768430","https://openalex.org/W2072128103","https://openalex.org/W2100495367","https://openalex.org/W2125485651","https://openalex.org/W2140249634","https://openalex.org/W2145882675","https://openalex.org/W2163922914","https://openalex.org/W2554205916","https://openalex.org/W2562512660","https://openalex.org/W4231109964"],"related_works":["https://openalex.org/W2159052453","https://openalex.org/W3013693939","https://openalex.org/W2566616303","https://openalex.org/W3131327266","https://openalex.org/W2910004569","https://openalex.org/W2669956259","https://openalex.org/W3201296424","https://openalex.org/W4249005693","https://openalex.org/W4365790226","https://openalex.org/W1991282161"],"abstract_inverted_index":{"Deep":[0],"multi-layer":[1],"neural":[2,107],"networks":[3,108],"are":[4],"generally":[5],"trained":[6],"using":[7],"variants":[8],"of":[9,18,24,52,145,151,173],"the":[10,50,53,89,116,120,125,137,143,149,162],"gradient":[11,38],"descent":[12],"based":[13,105],"algorithm.":[14],"However,":[15],"this":[16,110],"kind":[17],"algorithms":[19],"usually":[20,79],"encounter":[21],"a":[22,44,73,95,167],"series":[23],"shortcomings,":[25],"such":[26],"as":[27],"low":[28,126],"training":[29,131,174],"efficiency,":[30],"local":[31],"minimum,":[32],"difficult":[33],"control":[34,139],"parameter":[35],"tuning,":[36],"and":[37,64,77,83,97,148,176],"vanishing":[39],"or":[40],"exploding.":[41],"Besides,":[42],"for":[43],"specific":[45],"application,":[46],"how":[47,57,65],"to":[48,101,135],"design":[49],"structure":[51],"network,":[54],"that":[55,161],"is,":[56],"many":[58,66],"neurons":[59,152],"in":[60,85,109,153,171],"each":[61,154],"hidden":[62,67,146,155],"layer":[63],"layers":[68,147],"is":[69,71,78],"needed,":[70],"also":[72],"very":[74],"tricky":[75],"problem":[76],"solved":[80],"by":[81],"trial":[82],"error":[84],"practice.":[86],"To":[87],"overcome":[88],"shortcomings":[90],"mentioned":[91],"above,":[92],"we":[93],"present":[94],"fast":[96],"fully":[98],"automated":[99],"method":[100,114,164],"train":[102],"stacked":[103,117],"autoencoders":[104,118],"deep":[106],"paper.":[111],"The":[112,129,157],"proposed":[113,163],"trains":[115],"adopting":[119],"pseudoinverse":[121],"learning":[122,138],"algorithm":[123],"with":[124],"rank":[127],"approximation.":[128],"entire":[130],"process":[132],"neither":[133],"need":[134],"set":[136],"parameters,":[140],"nor":[141],"specify":[142],"number":[144,150],"layer.":[156],"experimental":[158],"results":[159],"show":[160],"can":[165],"achieve":[166],"comprehensively":[168],"better":[169],"performance":[170],"terms":[172],"efficiency":[175],"accuracy.":[177]},"counts_by_year":[{"year":2025,"cited_by_count":3},{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":4},{"year":2022,"cited_by_count":6},{"year":2021,"cited_by_count":8},{"year":2020,"cited_by_count":6},{"year":2019,"cited_by_count":4},{"year":2018,"cited_by_count":7},{"year":2017,"cited_by_count":1}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}