{"id":"https://openalex.org/W4226089757","doi":"https://doi.org/10.48550/arxiv.2202.02249","title":"Functional Mixtures-of-Experts","display_name":"Functional Mixtures-of-Experts","publication_year":2022,"publication_date":"2022-02-04","ids":{"openalex":"https://openalex.org/W4226089757","doi":"https://doi.org/10.48550/arxiv.2202.02249"},"language":"en","primary_location":{"id":"pmh:oai:arXiv.org:2202.02249","doi":null,"is_oa":true,"landing_page_url":"http://arxiv.org/abs/2202.02249","pdf_url":"https://arxiv.org/pdf/2202.02249","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"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"},"volume":null,"type":"preprint","indexed_in":["arxiv","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2202.02249","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5020695688","display_name":"Fa\u00efcel Chamroukhi","orcid":"https://orcid.org/0000-0002-5894-3103","observed_orcids":["https://orcid.org/0000-0002-5894-3103"]},"institutions":[{"id":"https://openalex.org/I1294671590","display_name":"Centre National de la Recherche Scientifique","ror":"https://ror.org/02feahw73","country_code":"FR","type":"government","lineage":["https://openalex.org/I1294671590"]},{"id":"https://openalex.org/I3018083178","display_name":"Institut de Recherche Technologique SystemX","ror":"https://ror.org/03crmsn52","country_code":"FR","type":"facility","lineage":["https://openalex.org/I3018083178"]},{"id":"https://openalex.org/I4210105918","display_name":"Normandie Universit\u00e9","ror":"https://ror.org/01k40cz91","country_code":"FR","type":"education","lineage":["https://openalex.org/I4210105918"]},{"id":"https://openalex.org/I98702875","display_name":"Universit\u00e9 de Caen Normandie","ror":"https://ror.org/051kpcy16","country_code":"FR","type":"education","lineage":["https://openalex.org/I4210105918","https://openalex.org/I98702875"]}],"countries":["FR"],"is_corresponding":false,"raw_author_name":"Chamroukhi, Fa\u00efcel","raw_affiliation_strings":["IRT SystemX , 2 boulevard Thomas Gobert , 91120 Palaiseau , France","Normandie Univ , UNICAEN , CNRS , LMNO , 14000 Caen , France"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"IRT SystemX , 2 boulevard Thomas Gobert , 91120 Palaiseau , France","institution_ids":["https://openalex.org/I3018083178"]},{"raw_affiliation_string":"Normandie Univ , UNICAEN , CNRS , LMNO , 14000 Caen , France","institution_ids":["https://openalex.org/I1294671590","https://openalex.org/I4210105918","https://openalex.org/I98702875"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5027483749","display_name":"Nhat Thien Pham","orcid":"https://orcid.org/0009-0003-5693-3141","observed_orcids":["https://orcid.org/0009-0003-5693-3141"]},"institutions":[{"id":"https://openalex.org/I1294671590","display_name":"Centre National de la Recherche Scientifique","ror":"https://ror.org/02feahw73","country_code":"FR","type":"government","lineage":["https://openalex.org/I1294671590"]},{"id":"https://openalex.org/I4210105918","display_name":"Normandie Universit\u00e9","ror":"https://ror.org/01k40cz91","country_code":"FR","type":"education","lineage":["https://openalex.org/I4210105918"]},{"id":"https://openalex.org/I98702875","display_name":"Universit\u00e9 de Caen Normandie","ror":"https://ror.org/051kpcy16","country_code":"FR","type":"education","lineage":["https://openalex.org/I4210105918","https://openalex.org/I98702875"]}],"countries":["FR"],"is_corresponding":false,"raw_author_name":"Pham, Nhat Thien","raw_affiliation_strings":["Normandie Univ , UNICAEN , CNRS , LMNO , 14000 Caen , France"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Normandie Univ , UNICAEN , CNRS , LMNO , 14000 Caen , France","institution_ids":["https://openalex.org/I1294671590","https://openalex.org/I4210105918","https://openalex.org/I98702875"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5084617491","display_name":"Van H\u00e0 Hoang","orcid":"https://orcid.org/0000-0002-6020-7981","observed_orcids":["https://orcid.org/0000-0002-6020-7981"]},"institutions":[{"id":"https://openalex.org/I23582244","display_name":"Ho Chi Minh City University of Science","ror":"https://ror.org/05jfbgm49","country_code":"VN","type":"education","lineage":["https://openalex.org/I123565023","https://openalex.org/I23582244"]}],"countries":["VN"],"is_corresponding":false,"raw_author_name":"Hoang, Van H\u00e0","raw_affiliation_strings":["University of Science , Vietnam National University , Ho Chi Minh City , Vietnam"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Science , Vietnam National University , Ho Chi Minh City , Vietnam","institution_ids":["https://openalex.org/I23582244"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5057375178","display_name":"Geoffrey John McLachlan","orcid":"https://orcid.org/0000-0002-5921-3145","observed_orcids":["https://orcid.org/0000-0002-5921-3145"]},"institutions":[{"id":"https://openalex.org/I165143802","display_name":"The University of Queensland","ror":"https://ror.org/00rqy9422","country_code":"AU","type":"education","lineage":["https://openalex.org/I165143802"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"McLachlan, Geoffrey J.","raw_affiliation_strings":["School of Mathematics and Physics , University of Queensland , Brisbane , Australia"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Mathematics and Physics , University of Queensland , Brisbane , Australia","institution_ids":["https://openalex.org/I165143802"]}]}],"institutions":[],"countries_distinct_count":3,"institutions_distinct_count":6,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":true,"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/T11901","display_name":"Bayesian Methods and Mixture Models","score":0.7710000276565552,"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/T11901","display_name":"Bayesian Methods and Mixture Models","score":0.7710000276565552,"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/T11871","display_name":"Advanced Statistical Methods and Models","score":0.15410000085830688,"subfield":{"id":"https://openalex.org/subfields/2613","display_name":"Statistics and Probability"},"field":{"id":"https://openalex.org/fields/26","display_name":"Mathematics"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10136","display_name":"Statistical Methods and Inference","score":0.01640000008046627,"subfield":{"id":"https://openalex.org/subfields/2613","display_name":"Statistics and Probability"},"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/functional-data-analysis","display_name":"functional data analysis","score":0.9760000109672546},{"id":"https://openalex.org/keywords/time-series-forecasting","display_name":"time series forecasting","score":0.6660000085830688},{"id":"https://openalex.org/keywords/data-heterogeneity","display_name":"data heterogeneity","score":0.5899999737739563},{"id":"https://openalex.org/keywords/lasso-regularization","display_name":"LASSO regularization","score":0.9010000228881836},{"id":"https://openalex.org/keywords/expectation-maximization-algorithm","display_name":"expectation-maximization algorithm","score":0.7730000019073486},{"id":"https://openalex.org/keywords/nonlinear-regression","display_name":"nonlinear regression","score":0.6100000143051147},{"id":"https://openalex.org/keywords/interpretable-models","display_name":"interpretable models","score":0.7260000109672546}],"concepts":[{"id":"https://openalex.org/C37616216","wikidata":"https://www.wikidata.org/wiki/Q3218363","display_name":"Lasso (programming language)","level":2,"score":0.6791061162948608},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6136913299560547},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.5124467015266418},{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.5044318437576294},{"id":"https://openalex.org/C41587187","wikidata":"https://www.wikidata.org/wiki/Q1501882","display_name":"Generalized linear model","level":2,"score":0.46930769085884094},{"id":"https://openalex.org/C182081679","wikidata":"https://www.wikidata.org/wiki/Q1275153","display_name":"Expectation\u2013maximization algorithm","level":3,"score":0.4461183547973633},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4260179400444031},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4165405035018921},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3954342007637024},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.37338507175445557},{"id":"https://openalex.org/C49781872","wikidata":"https://www.wikidata.org/wiki/Q1045555","display_name":"Maximum likelihood","level":2,"score":0.29150766134262085},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.28243041038513184},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.11915996670722961},{"id":"https://openalex.org/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","level":1,"score":0.0},{"id":"https://openalex.org/C136764020","wikidata":"https://www.wikidata.org/wiki/Q466","display_name":"World Wide Web","level":1,"score":0.0},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0}],"mesh":[],"locations_count":4,"locations":[{"id":"pmh:oai:arXiv.org:2202.02249","doi":null,"is_oa":true,"landing_page_url":"http://arxiv.org/abs/2202.02249","pdf_url":"https://arxiv.org/pdf/2202.02249","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"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"},{"id":"pmh:oai:HAL:hal-03584425v1","doi":null,"is_oa":false,"landing_page_url":"https://hal.science/hal-03584425v1","pdf_url":null,"source":{"id":"https://openalex.org/S4306402512","display_name":"HAL (Le Centre pour la Communication Scientifique Directe)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I1294671590","host_organization_name":"Centre National de la Recherche Scientifique","host_organization_lineage":["https://openalex.org/I1294671590"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"[Research Report] Normandie Univ, UNICAEN, CNRS, LMNO, 14000 Caen, France; University of Science, Vietnam National University, Ho Chi Minh City, Vietnam; School of Mathematics and Physics, University of Queensland, Brisbane, Australia. 2022","raw_type":"info:eu-repo/semantics/report"},{"id":"pmh:oai:HAL:hal-03584969v1","doi":null,"is_oa":false,"landing_page_url":"https://hal.science/hal-03584969v1","pdf_url":null,"source":{"id":"https://openalex.org/S4306402512","display_name":"HAL (Le Centre pour la Communication Scientifique Directe)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I1294671590","host_organization_name":"Centre National de la Recherche Scientifique","host_organization_lineage":["https://openalex.org/I1294671590"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"CRONOS Multivariate Data Analysis workshop. Model-based and multivariate functional data Session, Apr 2019, Limassol, Cyprus","raw_type":"info:eu-repo/semantics/conferenceObject"},{"id":"doi:10.48550/arxiv.2202.02249","doi":"https://doi.org/10.48550/arxiv.2202.02249","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2202.02249","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"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":"pmh:oai:arXiv.org:2202.02249","doi":null,"is_oa":true,"landing_page_url":"http://arxiv.org/abs/2202.02249","pdf_url":"https://arxiv.org/pdf/2202.02249","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"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":[],"sustainable_development_goals_aurora":[],"study_designs":[],"awards":[{"id":"https://openalex.org/G4656594441","display_name":null,"funder_award_id":"ANR18","funder_id":"https://openalex.org/F4320320883","funder_display_name":"Agence Nationale de la Recherche"},{"id":"https://openalex.org/G6138215733","display_name":null,"funder_award_id":"C2022-18-01","funder_id":"https://openalex.org/F4320326592","funder_display_name":"Viet Nam National University Ho Chi Minh City"},{"id":"https://openalex.org/G8868659442","display_name":"Statistical Modeling and Inference for unsupervised Learning at largE-Scale","funder_award_id":"ANR-18-CE40-0014","funder_id":"https://openalex.org/F4320320883","funder_display_name":"Agence Nationale de la Recherche"}],"funders":[{"id":"https://openalex.org/F4320320883","display_name":"Agence Nationale de la Recherche","ror":"https://ror.org/00rbzpz17"},{"id":"https://openalex.org/F4320326592","display_name":"Viet Nam National University Ho Chi Minh City","ror":"https://ror.org/00waaqh38"},{"id":"https://openalex.org/F4320329938","display_name":"R\u00e9gion Normandie","ror":"https://ror.org/03cq89j92"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W4256576576","https://openalex.org/W3090384609","https://openalex.org/W3194013178","https://openalex.org/W4249086404","https://openalex.org/W4236464082","https://openalex.org/W2790019364","https://openalex.org/W2212119398","https://openalex.org/W4298130764","https://openalex.org/W2374035137","https://openalex.org/W2020092793"],"abstract_inverted_index":{"We":[0,20,48,122],"consider":[1],"the":[2,13,22,63,73,78,81,102,117,137,158,173],"statistical":[3],"analysis":[4,46],"of":[5,30,54,77,101,116],"heterogeneous":[6,174],"data":[7,36,45,74,155],"for":[8,37,127],"prediction":[9,38],"in":[10,32,35,61,146,150,164,171],"situations":[11],"where":[12],"observations":[14],"include":[15],"functions,":[16],"typically":[17],"time":[18],"series.":[19],"extend":[21],"modeling":[23,33],"with":[24,39],"Mixtures-of-Experts":[25],"(ME),":[26],"as":[27],"a":[28,51,87],"framework":[29],"choice":[31],"heterogeneity":[34],"vectorial":[40],"observations,":[41,68],"to":[42,135,152],"this":[43],"functional":[44,58,104,114],"context.":[47],"first":[49],"present":[50],"new":[52],"family":[53],"ME":[55,59],"models,":[56],"named":[57],"(FME)":[60],"which":[62],"predictors":[64],"are":[65,144],"potentially":[66],"noisy":[67],"from":[69],"entire":[70],"functions.":[71],"Furthermore,":[72],"generating":[75],"process":[76],"predictor":[79],"and":[80,112,142,149,157,170],"real":[82,154],"response,":[83],"is":[84],"governed":[85],"by":[86,96],"hidden":[88],"discrete":[89],"variable":[90],"representing":[91],"an":[92],"unknown":[93],"partition.":[94],"Second,":[95],"imposing":[97],"sparsity":[98],"on":[99],"derivatives":[100],"underlying":[103],"parameters":[105],"via":[106],"Lasso-like":[107,128],"regularizations,":[108],"we":[109],"provide":[110],"sparse":[111],"interpretable":[113],"representations":[115],"FME":[118],"models":[119,141],"called":[120],"iFME.":[121],"develop":[123],"dedicated":[124],"expectation--maximization":[125],"algorithms":[126,143],"(EM-Lasso)":[129],"regularized":[130],"maximum-likelihood":[131],"parameter":[132],"estimation":[133],"strategies":[134],"fit":[136],"models.":[138],"The":[139],"proposed":[140],"studied":[145],"simulated":[147],"scenarios":[148],"applications":[151],"two":[153],"sets,":[156],"obtained":[159],"results":[160],"demonstrate":[161],"their":[162],"performance":[163],"accurately":[165],"capturing":[166],"complex":[167],"nonlinear":[168],"relationships":[169],"clustering":[172],"regression":[175],"data.":[176]},"counts_by_year":[],"updated_date":"2026-09-30T09:27:27.532063","created_date":"2025-10-10T00:00:00"}