{"id":"https://openalex.org/W2758695710","doi":"https://doi.org/10.1109/tnnls.2017.2747861","title":"Deep Learning of Constrained Autoencoders for Enhanced Understanding of Data","display_name":"Deep Learning of Constrained Autoencoders for Enhanced Understanding of Data","publication_year":2017,"publication_date":"2017-09-26","ids":{"openalex":"https://openalex.org/W2758695710","doi":"https://doi.org/10.1109/tnnls.2017.2747861","mag":"2758695710","pmid":"https://pubmed.ncbi.nlm.nih.gov/28961128"},"language":"en","primary_location":{"id":"doi:10.1109/tnnls.2017.2747861","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tnnls.2017.2747861","pdf_url":null,"source":{"id":"https://openalex.org/S4210175523","display_name":"IEEE Transactions on Neural Networks and Learning Systems","issn_l":"2162-237X","issn":["2162-237X","2162-2388"],"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 Neural Networks and Learning Systems","raw_type":"journal-article"},"type":"article","indexed_in":["arxiv","crossref","pubmed"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/1802.00003","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5037717507","display_name":"Babajide O. Ayinde","orcid":"https://orcid.org/0000-0001-7341-8799"},"institutions":[{"id":"https://openalex.org/I142740786","display_name":"University of Louisville","ror":"https://ror.org/01ckdn478","country_code":"US","type":"education","lineage":["https://openalex.org/I142740786"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Babajide O. Ayinde","raw_affiliation_strings":["Department of Electrical and Computer Engineering, University of Louisville, Louisville, KY, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Electrical and Computer Engineering, University of Louisville, Louisville, KY, USA","institution_ids":["https://openalex.org/I142740786"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5015925094","display_name":"Jacek M. \u017burada","orcid":"https://orcid.org/0000-0001-6622-534X"},"institutions":[{"id":"https://openalex.org/I142740786","display_name":"University of Louisville","ror":"https://ror.org/01ckdn478","country_code":"US","type":"education","lineage":["https://openalex.org/I142740786"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Jacek M. Zurada","raw_affiliation_strings":["Department of Electrical and Computer Engineering, University of Louisville, Louisville, KY, USA"],"raw_orcid":"https://orcid.org/0000-0001-6622-534X","affiliations":[{"raw_affiliation_string":"Department of Electrical and Computer Engineering, University of Louisville, Louisville, KY, USA","institution_ids":["https://openalex.org/I142740786"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I142740786"],"apc_list":null,"apc_paid":null,"fwci":2.8862,"has_fulltext":false,"cited_by_count":62,"citation_normalized_percentile":{"value":0.94825929,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":90,"max":100},"biblio":{"volume":"29","issue":"9","first_page":"3969","last_page":"3979"},"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":0.9990000128746033,"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":0.9990000128746033,"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/T10320","display_name":"Neural Networks and Applications","score":0.9904000163078308,"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/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9898999929428101,"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/mnist-database","display_name":"MNIST database","score":0.8571017384529114},{"id":"https://openalex.org/keywords/autoencoder","display_name":"Autoencoder","score":0.7833921909332275},{"id":"https://openalex.org/keywords/feature-learning","display_name":"Feature learning","score":0.647586464881897},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6414747834205627},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6307599544525146},{"id":"https://openalex.org/keywords/discriminative-model","display_name":"Discriminative model","score":0.6083884239196777},{"id":"https://openalex.org/keywords/regularization","display_name":"Regularization (linguistics)","score":0.6018362641334534},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5889089703559875},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.569992184638977},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.5675448179244995},{"id":"https://openalex.org/keywords/categorization","display_name":"Categorization","score":0.5054941177368164},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4174099564552307},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.364017516374588}],"concepts":[{"id":"https://openalex.org/C190502265","wikidata":"https://www.wikidata.org/wiki/Q17069496","display_name":"MNIST database","level":3,"score":0.8571017384529114},{"id":"https://openalex.org/C101738243","wikidata":"https://www.wikidata.org/wiki/Q786435","display_name":"Autoencoder","level":3,"score":0.7833921909332275},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.647586464881897},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6414747834205627},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6307599544525146},{"id":"https://openalex.org/C97931131","wikidata":"https://www.wikidata.org/wiki/Q5282087","display_name":"Discriminative model","level":2,"score":0.6083884239196777},{"id":"https://openalex.org/C2776135515","wikidata":"https://www.wikidata.org/wiki/Q17143721","display_name":"Regularization (linguistics)","level":2,"score":0.6018362641334534},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5889089703559875},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.569992184638977},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.5675448179244995},{"id":"https://openalex.org/C94124525","wikidata":"https://www.wikidata.org/wiki/Q912550","display_name":"Categorization","level":2,"score":0.5054941177368164},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4174099564552307},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.364017516374588},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"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/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","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},{"id":"https://openalex.org/C17744445","wikidata":"https://www.wikidata.org/wiki/Q36442","display_name":"Political science","level":0,"score":0.0}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.1109/tnnls.2017.2747861","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tnnls.2017.2747861","pdf_url":null,"source":{"id":"https://openalex.org/S4210175523","display_name":"IEEE Transactions on Neural Networks and Learning Systems","issn_l":"2162-237X","issn":["2162-237X","2162-2388"],"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 Neural Networks and Learning Systems","raw_type":"journal-article"},{"id":"pmid:28961128","is_oa":false,"landing_page_url":"https://pubmed.ncbi.nlm.nih.gov/28961128","pdf_url":null,"source":{"id":"https://openalex.org/S4306525036","display_name":"PubMed","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1299303238","host_organization_name":"National Institutes of Health","host_organization_lineage":["https://openalex.org/I1299303238"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE transactions on neural networks and learning systems","raw_type":null},{"id":"pmh:oai:arXiv.org:1802.00003","is_oa":true,"landing_page_url":"http://arxiv.org/abs/1802.00003","pdf_url":"https://arxiv.org/pdf/1802.00003","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"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":"text"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:1802.00003","is_oa":true,"landing_page_url":"http://arxiv.org/abs/1802.00003","pdf_url":"https://arxiv.org/pdf/1802.00003","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"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":"text"},"sustainable_development_goals":[{"display_name":"Reduced inequalities","score":0.7699999809265137,"id":"https://metadata.un.org/sdg/10"}],"awards":[{"id":"https://openalex.org/G5314776781","display_name":"EAGER: Additive Parts-based Data Representation with Nonnegative Sparse Autoencoders","funder_award_id":"1641042","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"}],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":43,"referenced_works":["https://openalex.org/W166926187","https://openalex.org/W1565377632","https://openalex.org/W1779483307","https://openalex.org/W1902027874","https://openalex.org/W1904365287","https://openalex.org/W1959608418","https://openalex.org/W2000359198","https://openalex.org/W2004536376","https://openalex.org/W2025768430","https://openalex.org/W2028069051","https://openalex.org/W2072128103","https://openalex.org/W2072599882","https://openalex.org/W2093522043","https://openalex.org/W2095705004","https://openalex.org/W2099579348","https://openalex.org/W2100495367","https://openalex.org/W2108665656","https://openalex.org/W2110798204","https://openalex.org/W2112796928","https://openalex.org/W2123585936","https://openalex.org/W2134557905","https://openalex.org/W2144513243","https://openalex.org/W2145889472","https://openalex.org/W2153989749","https://openalex.org/W2187089797","https://openalex.org/W2189422931","https://openalex.org/W2474601476","https://openalex.org/W2556893404","https://openalex.org/W2569517805","https://openalex.org/W2613634265","https://openalex.org/W2963275229","https://openalex.org/W3029645440","https://openalex.org/W4205774955","https://openalex.org/W4231109964","https://openalex.org/W4293568373","https://openalex.org/W4293775970","https://openalex.org/W6606792154","https://openalex.org/W6674330103","https://openalex.org/W6676231525","https://openalex.org/W6676481782","https://openalex.org/W6681151457","https://openalex.org/W6721237244","https://openalex.org/W6737658843"],"related_works":["https://openalex.org/W4394785709","https://openalex.org/W4296978181","https://openalex.org/W2912987408","https://openalex.org/W3004801820","https://openalex.org/W4281672036","https://openalex.org/W4313444753","https://openalex.org/W4230582276","https://openalex.org/W2983142544","https://openalex.org/W2891059443","https://openalex.org/W2937381246"],"abstract_inverted_index":{"Unsupervised":[0],"feature":[1,119],"extractors":[2],"are":[3,39,103],"known":[4],"to":[5,23,45,80],"perform":[6,19],"an":[7],"efficient":[8],"and":[9,20,63,106,118,131],"discriminative":[10],"representation":[11],"of":[12,52,69,72],"data.":[13],"Insight":[14],"into":[15,85],"the":[16,50,73,76,122,125,132],"mappings":[17],"they":[18],"human":[21],"ability":[22],"understand":[24],"them,":[25],"however,":[26],"remain":[27],"very":[28],"limited.":[29],"This":[30,41],"is":[31,57,114],"especially":[32],"prominent":[33],"when":[34],"multilayer":[35],"deep":[36],"learning":[37],"architectures":[38],"used.":[40],"paper":[42],"demonstrates":[43],"how":[44],"remove":[46],"these":[47],"bottlenecks":[48],"within":[49],"architecture":[51],"non-negativity":[53,68],"constrained":[54,79],"autoencoder.":[55],"It":[56],"shown":[58],"that":[59,66,102],"using":[60],"both":[61],"L1":[62],"L2":[64],"regularizations":[65],"induce":[67],"weights,":[70],"most":[71],"weights":[74],"in":[75,93],"network":[77],"become":[78],"be":[81],"non-negative,":[82],"thereby":[83],"resulting":[84],"a":[86],"more":[87,104],"understandable":[88],"structure":[89],"with":[90],"minute":[91],"deterioration":[92],"classification":[94],"accuracy.":[95],"Also,":[96],"this":[97],"proposed":[98],"approach":[99],"extracts":[100],"features":[101],"sparse":[105],"produces":[107],"additional":[108],"output":[109],"layer":[110],"sparsification.":[111],"The":[112],"method":[113],"analyzed":[115],"for":[116],"accuracy":[117],"interpretation":[120],"on":[121],"MNIST":[123],"data,":[124,130],"NORB":[126],"normalized":[127],"uniform":[128],"object":[129],"Reuters":[133],"text":[134],"categorization":[135],"data":[136],"set.":[137]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":3},{"year":2024,"cited_by_count":7},{"year":2023,"cited_by_count":7},{"year":2022,"cited_by_count":6},{"year":2021,"cited_by_count":6},{"year":2020,"cited_by_count":9},{"year":2019,"cited_by_count":20},{"year":2018,"cited_by_count":1},{"year":2017,"cited_by_count":2}],"updated_date":"2026-03-27T05:58:40.876381","created_date":"2025-10-10T00:00:00"}