{"id":"https://openalex.org/W2546323938","doi":"https://doi.org/10.1109/tnnls.2016.2616145","title":"Distinct Variation Pattern Discovery Using Alternating Nonlinear Principal Component Analysis","display_name":"Distinct Variation Pattern Discovery Using Alternating Nonlinear Principal Component Analysis","publication_year":2016,"publication_date":"2016-10-26","ids":{"openalex":"https://openalex.org/W2546323938","doi":"https://doi.org/10.1109/tnnls.2016.2616145","mag":"2546323938","pmid":"https://pubmed.ncbi.nlm.nih.gov/27810837"},"language":"en","primary_location":{"id":"doi:10.1109/tnnls.2016.2616145","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tnnls.2016.2616145","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":["crossref","pubmed"],"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/A5062674186","display_name":"Phillip Howard","orcid":"https://orcid.org/0000-0003-0529-7483"},"institutions":[{"id":"https://openalex.org/I55732556","display_name":"Arizona State University","ror":"https://ror.org/03efmqc40","country_code":"US","type":"education","lineage":["https://openalex.org/I55732556"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Phillip Howard","raw_affiliation_strings":["School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, Tempe, AZ, USA"],"raw_orcid":"https://orcid.org/0000-0003-0529-7483","affiliations":[{"raw_affiliation_string":"School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, Tempe, AZ, USA","institution_ids":["https://openalex.org/I55732556"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5050269446","display_name":"Daniel W. Apley","orcid":"https://orcid.org/0000-0002-8545-4612"},"institutions":[{"id":"https://openalex.org/I111979921","display_name":"Northwestern University","ror":"https://ror.org/000e0be47","country_code":"US","type":"education","lineage":["https://openalex.org/I111979921"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Daniel W. Apley","raw_affiliation_strings":["Department of Industrial Engineering and Management Sciences, Northwestern University, Evanston, IL, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Industrial Engineering and Management Sciences, Northwestern University, Evanston, IL, USA","institution_ids":["https://openalex.org/I111979921"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5002544391","display_name":"George C. Runger","orcid":"https://orcid.org/0000-0001-9460-6983"},"institutions":[{"id":"https://openalex.org/I55732556","display_name":"Arizona State University","ror":"https://ror.org/03efmqc40","country_code":"US","type":"education","lineage":["https://openalex.org/I55732556"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"George Runger","raw_affiliation_strings":["School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, Tempe, AZ, USA"],"raw_orcid":"https://orcid.org/0000-0001-9460-6983","affiliations":[{"raw_affiliation_string":"School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, Tempe, AZ, USA","institution_ids":["https://openalex.org/I55732556"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.6761,"has_fulltext":false,"cited_by_count":10,"citation_normalized_percentile":{"value":0.77970313,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":96},"biblio":{"volume":"29","issue":"1","first_page":"156","last_page":"166"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T14339","display_name":"Image Processing and 3D Reconstruction","score":0.9954000115394592,"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/T14339","display_name":"Image Processing and 3D Reconstruction","score":0.9954000115394592,"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":0.9925000071525574,"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/T10640","display_name":"Spectroscopy and Chemometric Analyses","score":0.9904000163078308,"subfield":{"id":"https://openalex.org/subfields/1602","display_name":"Analytical Chemistry"},"field":{"id":"https://openalex.org/fields/16","display_name":"Chemistry"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/principal-component-analysis","display_name":"Principal component analysis","score":0.8801499605178833},{"id":"https://openalex.org/keywords/mnist-database","display_name":"MNIST database","score":0.8085941076278687},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.7418489456176758},{"id":"https://openalex.org/keywords/interpretability","display_name":"Interpretability","score":0.6775540113449097},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5887810587882996},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.5804244875907898},{"id":"https://openalex.org/keywords/variation","display_name":"Variation (astronomy)","score":0.534146249294281},{"id":"https://openalex.org/keywords/backpropagation","display_name":"Backpropagation","score":0.5259031653404236},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5246978998184204},{"id":"https://openalex.org/keywords/nonlinear-system","display_name":"Nonlinear system","score":0.5056431293487549},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.34715837240219116}],"concepts":[{"id":"https://openalex.org/C27438332","wikidata":"https://www.wikidata.org/wiki/Q2873","display_name":"Principal component analysis","level":2,"score":0.8801499605178833},{"id":"https://openalex.org/C190502265","wikidata":"https://www.wikidata.org/wiki/Q17069496","display_name":"MNIST database","level":3,"score":0.8085941076278687},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.7418489456176758},{"id":"https://openalex.org/C2781067378","wikidata":"https://www.wikidata.org/wiki/Q17027399","display_name":"Interpretability","level":2,"score":0.6775540113449097},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5887810587882996},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5804244875907898},{"id":"https://openalex.org/C2778334786","wikidata":"https://www.wikidata.org/wiki/Q1586270","display_name":"Variation (astronomy)","level":2,"score":0.534146249294281},{"id":"https://openalex.org/C155032097","wikidata":"https://www.wikidata.org/wiki/Q798503","display_name":"Backpropagation","level":3,"score":0.5259031653404236},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5246978998184204},{"id":"https://openalex.org/C158622935","wikidata":"https://www.wikidata.org/wiki/Q660848","display_name":"Nonlinear system","level":2,"score":0.5056431293487549},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.34715837240219116},{"id":"https://openalex.org/C44870925","wikidata":"https://www.wikidata.org/wiki/Q37547","display_name":"Astrophysics","level":1,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/tnnls.2016.2616145","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tnnls.2016.2616145","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:27810837","is_oa":false,"landing_page_url":"https://pubmed.ncbi.nlm.nih.gov/27810837","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}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G2920046245","display_name":null,"funder_award_id":"1265713","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":22,"referenced_works":["https://openalex.org/W62275428","https://openalex.org/W150173342","https://openalex.org/W879878326","https://openalex.org/W1574202531","https://openalex.org/W1974688106","https://openalex.org/W1980574926","https://openalex.org/W2010877658","https://openalex.org/W2024207974","https://openalex.org/W2068528719","https://openalex.org/W2082185517","https://openalex.org/W2099741732","https://openalex.org/W2100495367","https://openalex.org/W2122538988","https://openalex.org/W2137291015","https://openalex.org/W2140095548","https://openalex.org/W2172007398","https://openalex.org/W2803203065","https://openalex.org/W4246802270","https://openalex.org/W6606047813","https://openalex.org/W6634351063","https://openalex.org/W6680112165","https://openalex.org/W6751342432"],"related_works":["https://openalex.org/W4386603768","https://openalex.org/W2950475743","https://openalex.org/W2886711096","https://openalex.org/W2750384547","https://openalex.org/W4380078352","https://openalex.org/W2905433371","https://openalex.org/W3046591097","https://openalex.org/W3008580913","https://openalex.org/W1701967867","https://openalex.org/W2042813572"],"abstract_inverted_index":{"Autoassociative":[0],"neural":[1],"networks":[2],"(ANNs)":[3],"have":[4],"been":[5],"proposed":[6,94],"as":[7,123,125],"a":[8,117,126],"nonlinear":[9,70,102],"extension":[10],"of":[11,44,58,76,88,113,128],"principal":[12,29,103],"component":[13,30],"analysis":[14],"(PCA),":[15],"which":[16,48,73,100],"is":[17,49,93],"commonly":[18],"used":[19],"to":[20,99],"identify":[21],"linear":[22],"variation":[23,60,107,144],"patterns":[24,61,162],"in":[25,62,79,91,146,166],"high-dimensional":[26],"data.":[27,64,133],"While":[28],"scores":[31],"represent":[32,105],"uncorrelated":[33],"features,":[34,47],"standard":[35,138],"backpropagation":[36],"methods":[37],"for":[38,51,54,95],"training":[39],"ANNs":[40,139],"provide":[41],"no":[42],"guarantee":[43],"producing":[45],"distinct":[46,77,106],"important":[50],"interpretability":[52],"and":[53],"discovering":[55],"the":[56,59,63,86,97,101,111,129,142,147,152,161,167],"nature":[57],"Here,":[65],"we":[66],"present":[67],"an":[68],"alternating":[69,156],"PCA":[71,92],"method,":[72],"encourages":[74],"learning":[75],"features":[78],"ANNs.":[80],"A":[81],"new":[82],"measure":[83],"motivated":[84],"by":[85,151],"condition":[87],"orthogonal":[89],"loadings":[90],"measuring":[96],"extent":[98],"components":[104],"patterns.":[108],"We":[109],"demonstrate":[110],"effectiveness":[112],"our":[114,155],"method":[115,157],"using":[116],"simulated":[118],"point":[119],"cloud":[120],"data":[121],"set":[122],"well":[124],"subset":[127],"MNIST":[130],"handwritten":[131],"digits":[132],"The":[134],"results":[135],"show":[136],"that":[137],"consistently":[140],"mix":[141],"true":[143],"sources":[145],"low-dimensional":[148,168],"representation":[149],"learned":[150],"model,":[153],"whereas":[154],"produces":[158],"solutions":[159],"where":[160],"are":[163],"better":[164],"separated":[165],"space.":[169]},"counts_by_year":[{"year":2024,"cited_by_count":1},{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":2},{"year":2020,"cited_by_count":2},{"year":2019,"cited_by_count":2},{"year":2018,"cited_by_count":1},{"year":2016,"cited_by_count":1}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}