{"id":"https://openalex.org/W7163578279","doi":"https://doi.org/10.48550/arxiv.2606.04408","title":"An Ensembled Latent Factor Model via Differential Evolution and Gradient Descent Optimization","display_name":"An Ensembled Latent Factor Model via Differential Evolution and Gradient Descent Optimization","publication_year":2026,"publication_date":"2026-06-03","ids":{"openalex":"https://openalex.org/W7163578279","doi":"https://doi.org/10.48550/arxiv.2606.04408"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2606.04408","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.04408","pdf_url":null,"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":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2606.04408","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5137912499","display_name":"Rui Zhang (13940)","orcid":"https://orcid.org/0000-0003-4859-2968"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhang, Rui","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5137885871","display_name":"Jinhang Liu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Liu, Jinhang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5137837605","display_name":"Wenbo Zhang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhang, Wenbo","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12535","display_name":"Machine Learning and Data Classification","score":0.3716999888420105,"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/T12535","display_name":"Machine Learning and Data Classification","score":0.3716999888420105,"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/T10848","display_name":"Advanced Multi-Objective Optimization Algorithms","score":0.1145000010728836,"subfield":{"id":"https://openalex.org/subfields/1703","display_name":"Computational Theory and Mathematics"},"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/T12303","display_name":"Tensor decomposition and applications","score":0.07440000027418137,"subfield":{"id":"https://openalex.org/subfields/2605","display_name":"Computational Mathematics"},"field":{"id":"https://openalex.org/fields/26","display_name":"Mathematics"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/gradient-descent","display_name":"Gradient descent","score":0.6281999945640564},{"id":"https://openalex.org/keywords/weighting","display_name":"Weighting","score":0.5390999913215637},{"id":"https://openalex.org/keywords/factor","display_name":"Factor (programming language)","score":0.48919999599456787},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.47540000081062317},{"id":"https://openalex.org/keywords/factor-analysis","display_name":"Factor analysis","score":0.4171000123023987},{"id":"https://openalex.org/keywords/latent-class-model","display_name":"Latent class model","score":0.4004000127315521},{"id":"https://openalex.org/keywords/latent-variable","display_name":"Latent variable","score":0.39070001244544983},{"id":"https://openalex.org/keywords/differential-evolution","display_name":"Differential evolution","score":0.3612000048160553}],"concepts":[{"id":"https://openalex.org/C153258448","wikidata":"https://www.wikidata.org/wiki/Q1199743","display_name":"Gradient descent","level":3,"score":0.6281999945640564},{"id":"https://openalex.org/C183115368","wikidata":"https://www.wikidata.org/wiki/Q856577","display_name":"Weighting","level":2,"score":0.5390999913215637},{"id":"https://openalex.org/C2781039887","wikidata":"https://www.wikidata.org/wiki/Q1391724","display_name":"Factor (programming language)","level":2,"score":0.48919999599456787},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.47540000081062317},{"id":"https://openalex.org/C10879293","wikidata":"https://www.wikidata.org/wiki/Q726474","display_name":"Factor analysis","level":2,"score":0.4171000123023987},{"id":"https://openalex.org/C70727504","wikidata":"https://www.wikidata.org/wiki/Q1806878","display_name":"Latent class model","level":2,"score":0.4004000127315521},{"id":"https://openalex.org/C51167844","wikidata":"https://www.wikidata.org/wiki/Q4422623","display_name":"Latent variable","level":2,"score":0.39070001244544983},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.37689998745918274},{"id":"https://openalex.org/C74750220","wikidata":"https://www.wikidata.org/wiki/Q2662197","display_name":"Differential evolution","level":2,"score":0.3612000048160553},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.34209999442100525},{"id":"https://openalex.org/C2776637919","wikidata":"https://www.wikidata.org/wiki/Q624380","display_name":"Descent (aeronautics)","level":2,"score":0.323199987411499},{"id":"https://openalex.org/C93226319","wikidata":"https://www.wikidata.org/wiki/Q193137","display_name":"Differential (mechanical device)","level":2,"score":0.3151000142097473},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3091999888420105},{"id":"https://openalex.org/C137836250","wikidata":"https://www.wikidata.org/wiki/Q984063","display_name":"Optimization problem","level":2,"score":0.30550000071525574},{"id":"https://openalex.org/C65965080","wikidata":"https://www.wikidata.org/wiki/Q1806885","display_name":"Latent variable model","level":3,"score":0.2824999988079071},{"id":"https://openalex.org/C70136482","wikidata":"https://www.wikidata.org/wiki/Q13583781","display_name":"A-weighting","level":3,"score":0.27079999446868896},{"id":"https://openalex.org/C206688291","wikidata":"https://www.wikidata.org/wiki/Q7617819","display_name":"Stochastic gradient descent","level":3,"score":0.25999999046325684},{"id":"https://openalex.org/C2777303404","wikidata":"https://www.wikidata.org/wiki/Q759757","display_name":"Convergence (economics)","level":2,"score":0.2578999996185303},{"id":"https://openalex.org/C61224824","wikidata":"https://www.wikidata.org/wiki/Q2260434","display_name":"Mixture model","level":2,"score":0.25609999895095825}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2606.04408","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.04408","pdf_url":null,"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":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2606.04408","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.04408","pdf_url":null,"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":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"High-dimensional":[0],"and":[1,50,72,92,97,134],"incomplete":[2],"(HDI)":[3],"data":[4,11],"are":[5,86,105,144],"prevalent":[6],"in":[7],"many":[8],"real-world":[9],"big":[10],"scenarios.":[12],"Latent":[13,66],"factor":[14,36,84,103,157],"models":[15,37,85,104],"serve":[16],"as":[17],"a":[18,108],"common":[19],"representation":[20],"learning":[21],"approach,":[22],"capable":[23],"of":[24,123],"uncovering":[25],"informative":[26],"latent":[27,35,83,102,156],"factors":[28],"from":[29],"such":[30],"data.":[31,59,140],"Nevertheless,":[32],"most":[33],"existing":[34],"rely":[38],"solely":[39],"on":[40],"gradient":[41,93],"descent":[42,94],"for":[43,138],"optimization,":[44,95],"which":[45],"may":[46],"lead":[47],"to":[48,113,130,146],"insufficient":[49],"biased":[51,136],"representations,":[52],"particularly":[53],"when":[54],"dealing":[55],"with":[56,77],"heterogeneous":[57],"HDI":[58,139,142],"Thus,":[60],"this":[61],"study":[62],"proposes":[63],"an":[64],"Ensembled":[65],"Factor":[67],"Model":[68],"via":[69,89,107],"Differential":[70],"Evolution":[71],"Gradient":[73],"Descent":[74],"Optimization":[75],"(ELFM-DEGDO)":[76],"two-fold":[78],"designed:":[79],"1)":[80],"two":[81,100],"diverse":[82,101],"independently":[87],"modeled":[88],"differential":[90],"evolution":[91],"respectively,":[96],"2)":[98],"the":[99,120],"combined":[106],"customized":[109],"self-adaptive":[110],"weighting":[111],"mechanism":[112],"effectively":[114],"fuse":[115],"their":[116],"strengths.":[117],"By":[118],"leveraging":[119],"complementary":[121],"advantages":[122],"both":[124],"optimization":[125],"paradigms,":[126],"ELFM-DEGDO":[127,149],"is":[128],"able":[129],"produce":[131],"more":[132],"comprehensive":[133],"less":[135],"representations":[137],"Three":[141],"datasets":[143],"tested":[145],"show":[147],"that":[148],"consistently":[150],"performs":[151],"better":[152],"than":[153],"related":[154],"several":[155],"models.":[158]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-06-05T00:00:00"}