{"id":"https://openalex.org/W4409356084","doi":"https://doi.org/10.1109/tai.2025.3560248","title":"Deep Residual Learning of a Probabilistic\u2019 Partial Least Squares Model for Predictive Data Analytics","display_name":"Deep Residual Learning of a Probabilistic\u2019 Partial Least Squares Model for Predictive Data Analytics","publication_year":2025,"publication_date":"2025-04-11","ids":{"openalex":"https://openalex.org/W4409356084","doi":"https://doi.org/10.1109/tai.2025.3560248"},"language":"en","primary_location":{"id":"doi:10.1109/tai.2025.3560248","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tai.2025.3560248","pdf_url":null,"source":{"id":"https://openalex.org/S4210169448","display_name":"IEEE Transactions on Artificial Intelligence","issn_l":"2691-4581","issn":["2691-4581"],"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 Artificial Intelligence","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/A5067726465","display_name":"Zhiqiang Ge","orcid":"https://orcid.org/0000-0002-2071-4380"},"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":true,"raw_author_name":"Zhiqiang Ge","raw_affiliation_strings":["School of Mathematics, Southeast University, Nanjing, China"],"affiliations":[{"raw_affiliation_string":"School of Mathematics, Southeast University, Nanjing, China","institution_ids":["https://openalex.org/I76569877"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5038398834","display_name":"Duxin Chen","orcid":"https://orcid.org/0000-0002-3194-2258"},"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":"Duxin Chen","raw_affiliation_strings":["School of Mathematics, Southeast University, Nanjing, China"],"affiliations":[{"raw_affiliation_string":"School of Mathematics, Southeast University, Nanjing, China","institution_ids":["https://openalex.org/I76569877"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5113146340","display_name":"Wenwu Yu","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":"Wenwu Yu","raw_affiliation_strings":["School of Mathematics, Southeast University, Nanjing, China"],"affiliations":[{"raw_affiliation_string":"School of Mathematics, Southeast University, Nanjing, China","institution_ids":["https://openalex.org/I76569877"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5067726465"],"corresponding_institution_ids":["https://openalex.org/I76569877"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.03160878,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"6","issue":"11","first_page":"2977","last_page":"2989"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10320","display_name":"Neural Networks and Applications","score":0.717199981212616,"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":0.717199981212616,"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/T10876","display_name":"Fault Detection and Control Systems","score":0.7098000049591064,"subfield":{"id":"https://openalex.org/subfields/2207","display_name":"Control and Systems Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.6330000162124634,"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/partial-least-squares-regression","display_name":"Partial least squares regression","score":0.7466043829917908},{"id":"https://openalex.org/keywords/residual","display_name":"Residual","score":0.7167997360229492},{"id":"https://openalex.org/keywords/predictive-analytics","display_name":"Predictive analytics","score":0.5394023656845093},{"id":"https://openalex.org/keywords/analytics","display_name":"Analytics","score":0.5113029479980469},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5042968988418579},{"id":"https://openalex.org/keywords/probabilistic-logic","display_name":"Probabilistic logic","score":0.490178644657135},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4417183995246887},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.401711642742157},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.3586609959602356},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.32150930166244507},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.2627992033958435},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.18274518847465515}],"concepts":[{"id":"https://openalex.org/C22354355","wikidata":"https://www.wikidata.org/wiki/Q422009","display_name":"Partial least squares regression","level":2,"score":0.7466043829917908},{"id":"https://openalex.org/C155512373","wikidata":"https://www.wikidata.org/wiki/Q287450","display_name":"Residual","level":2,"score":0.7167997360229492},{"id":"https://openalex.org/C83209312","wikidata":"https://www.wikidata.org/wiki/Q1053367","display_name":"Predictive analytics","level":2,"score":0.5394023656845093},{"id":"https://openalex.org/C79158427","wikidata":"https://www.wikidata.org/wiki/Q485396","display_name":"Analytics","level":2,"score":0.5113029479980469},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5042968988418579},{"id":"https://openalex.org/C49937458","wikidata":"https://www.wikidata.org/wiki/Q2599292","display_name":"Probabilistic logic","level":2,"score":0.490178644657135},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4417183995246887},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.401711642742157},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.3586609959602356},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.32150930166244507},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.2627992033958435},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.18274518847465515}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tai.2025.3560248","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tai.2025.3560248","pdf_url":null,"source":{"id":"https://openalex.org/S4210169448","display_name":"IEEE Transactions on Artificial Intelligence","issn_l":"2691-4581","issn":["2691-4581"],"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 Artificial Intelligence","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Reduced inequalities","score":0.4099999964237213,"id":"https://metadata.un.org/sdg/10"}],"awards":[{"id":"https://openalex.org/G1528277265","display_name":null,"funder_award_id":"62273090","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G7846504898","display_name":null,"funder_award_id":"62233004","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":41,"referenced_works":["https://openalex.org/W1985690171","https://openalex.org/W2076423279","https://openalex.org/W2509008543","https://openalex.org/W2803546399","https://openalex.org/W2889017122","https://openalex.org/W2891711161","https://openalex.org/W2906787390","https://openalex.org/W2991860176","https://openalex.org/W2998450902","https://openalex.org/W3008278010","https://openalex.org/W3016192422","https://openalex.org/W3043554402","https://openalex.org/W3081318531","https://openalex.org/W3111476168","https://openalex.org/W3120241129","https://openalex.org/W3123899295","https://openalex.org/W4206073508","https://openalex.org/W4210772810","https://openalex.org/W4220952101","https://openalex.org/W4225270114","https://openalex.org/W4281394637","https://openalex.org/W4281962911","https://openalex.org/W4285259398","https://openalex.org/W4293146242","https://openalex.org/W4294811335","https://openalex.org/W4295242194","https://openalex.org/W4300960307","https://openalex.org/W4312678682","https://openalex.org/W4312916496","https://openalex.org/W4318149011","https://openalex.org/W4323922461","https://openalex.org/W4366493045","https://openalex.org/W4383200192","https://openalex.org/W4387917971","https://openalex.org/W4387987005","https://openalex.org/W4388505146","https://openalex.org/W4395056455","https://openalex.org/W4400111988","https://openalex.org/W4400810016","https://openalex.org/W4404810227","https://openalex.org/W4406833738"],"related_works":["https://openalex.org/W2570647323","https://openalex.org/W2206805568","https://openalex.org/W2076942471","https://openalex.org/W2863268765","https://openalex.org/W3027285423","https://openalex.org/W2896245927","https://openalex.org/W4205879366","https://openalex.org/W1961101704","https://openalex.org/W4254129905","https://openalex.org/W2414447594"],"abstract_inverted_index":{"Recently,":[0],"probabilistic":[1,53,105,175],"latent":[2,61,87,117],"variable":[3],"models":[4],"have":[5],"played":[6],"an":[7,103,135],"important":[8],"role":[9],"in":[10,13,63,143],"data":[11],"analytics":[12],"various":[14],"industrial":[15,169],"application":[16],"scenarios,":[17],"such":[18],"as":[19,134],"process":[20],"monitoring,":[21],"fault":[22],"diagnosis,":[23],"and":[24,112,147],"soft":[25],"sensing.":[26],"Inspired":[27],"by":[28,95,186],"the":[29,45,68,96,99,124,173,177,180],"idea":[30],"of":[31,67,98,115,160,179],"lightweight":[32],"deep":[33,40,69,181],"learning,":[34],"this":[35,80,129],"paper":[36],"proposes":[37],"a":[38,72],"new":[39],"residual":[41,82],"learning":[42,83,137],"method":[43],"for":[44,76,109,157],"probabilistic\u2019":[46],"partial":[47],"least":[48],"squares":[49],"model.":[50,101],"Firstly,":[51],"layer-wise":[52,81],"modeling":[54,125,145],"is":[55,107,154,165],"carried":[56],"out":[57],"to":[58,123,172],"extract":[59],"supervised":[60,94,116],"variables":[62,88,118],"different":[64],"hidden":[65],"layers":[66],"model":[70,106,182],"using":[71,167],"well-designed":[73],"expectation-maximization":[74],"algorithm":[75],"parameter":[77],"optimization.":[78],"Through":[79],"process,":[84],"more":[85],"target-related":[86],"can":[89,131],"be":[90,132],"extracted,":[91],"which":[92,119,139],"are":[93,120],"outputs":[97],"predictive":[100],"Next,":[102],"additional":[104],"constructed":[108],"information":[110],"fusion":[111],"further":[113],"extraction":[114],"highly":[121],"related":[122],"target.":[126],"In":[127],"fact,":[128],"step":[130],"considered":[133],"ensemble":[136],"strategy,":[138],"has":[140,183],"great":[141],"potentials":[142],"decreasing":[144],"error":[146],"reducing":[148],"prediction":[149,159],"uncertainty.":[150],"A":[151],"soft-sensing":[152],"strategy":[153],"then":[155],"developed":[156],"online":[158],"key":[161],"variables.":[162],"The":[163],"performance":[164,178],"evaluated":[166],"two":[168],"examples.":[170],"Compared":[171],"shallow":[174],"model,":[176],"been":[184],"improved":[185],"10%-20%.":[187]},"counts_by_year":[],"updated_date":"2026-04-09T08:11:56.329763","created_date":"2025-10-10T00:00:00"}