{"id":"https://openalex.org/W3094624470","doi":"https://doi.org/10.1145/3412815.3416894","title":"Dynamical Gaussian Process Latent Variable Model for Representation Learning from Longitudinal Data","display_name":"Dynamical Gaussian Process Latent Variable Model for Representation Learning from Longitudinal Data","publication_year":2020,"publication_date":"2020-10-15","ids":{"openalex":"https://openalex.org/W3094624470","doi":"https://doi.org/10.1145/3412815.3416894","mag":"3094624470"},"language":"en","primary_location":{"id":"doi:10.1145/3412815.3416894","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3412815.3416894","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2020 ACM-IMS on Foundations of Data Science Conference","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://scholarsphere.psu.edu/resources/cd882941-dd70-478e-9704-80ab882670a1/downloads/16832","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5064952452","display_name":"Thanh Le","orcid":"https://orcid.org/0000-0002-2180-4222"},"institutions":[{"id":"https://openalex.org/I130769515","display_name":"Pennsylvania State University","ror":"https://ror.org/04p491231","country_code":"US","type":"education","lineage":["https://openalex.org/I130769515"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Thanh Le","raw_affiliation_strings":["Pennsylvania State University, University Park, PA, USA"],"affiliations":[{"raw_affiliation_string":"Pennsylvania State University, University Park, PA, USA","institution_ids":["https://openalex.org/I130769515"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5004737962","display_name":"Vasant Honavar","orcid":"https://orcid.org/0000-0001-5399-3489"},"institutions":[{"id":"https://openalex.org/I130769515","display_name":"Pennsylvania State University","ror":"https://ror.org/04p491231","country_code":"US","type":"education","lineage":["https://openalex.org/I130769515"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Vasant Honavar","raw_affiliation_strings":["Pennsylvania State University, University Park, PA, USA"],"affiliations":[{"raw_affiliation_string":"Pennsylvania State University, University Park, PA, USA","institution_ids":["https://openalex.org/I130769515"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5064952452"],"corresponding_institution_ids":["https://openalex.org/I130769515"],"apc_list":null,"apc_paid":null,"fwci":0.4114,"has_fulltext":false,"cited_by_count":4,"citation_normalized_percentile":{"value":0.70397251,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":95},"biblio":{"volume":"21","issue":null,"first_page":"183","last_page":"188"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12814","display_name":"Gaussian Processes and Bayesian Inference","score":0.9993000030517578,"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/T12814","display_name":"Gaussian Processes and Bayesian Inference","score":0.9993000030517578,"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/T12120","display_name":"Air Quality Monitoring and Forecasting","score":0.9661999940872192,"subfield":{"id":"https://openalex.org/subfields/2305","display_name":"Environmental Engineering"},"field":{"id":"https://openalex.org/fields/23","display_name":"Environmental Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T14393","display_name":"Health, Environment, Cognitive Aging","score":0.9646000266075134,"subfield":{"id":"https://openalex.org/subfields/2307","display_name":"Health, Toxicology and Mutagenesis"},"field":{"id":"https://openalex.org/fields/23","display_name":"Environmental Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/latent-variable","display_name":"Latent variable","score":0.741263747215271},{"id":"https://openalex.org/keywords/gaussian-process","display_name":"Gaussian process","score":0.6294645667076111},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5676173567771912},{"id":"https://openalex.org/keywords/gaussian","display_name":"Gaussian","score":0.4889008104801178},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4872209131717682},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4498245120048523},{"id":"https://openalex.org/keywords/cluster-analysis","display_name":"Cluster analysis","score":0.44628968834877014},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.44037312269210815},{"id":"https://openalex.org/keywords/kriging","display_name":"Kriging","score":0.4215683341026306},{"id":"https://openalex.org/keywords/latent-variable-model","display_name":"Latent variable model","score":0.41836339235305786},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.4178752303123474},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3977309763431549}],"concepts":[{"id":"https://openalex.org/C51167844","wikidata":"https://www.wikidata.org/wiki/Q4422623","display_name":"Latent variable","level":2,"score":0.741263747215271},{"id":"https://openalex.org/C61326573","wikidata":"https://www.wikidata.org/wiki/Q1496376","display_name":"Gaussian process","level":3,"score":0.6294645667076111},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5676173567771912},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.4889008104801178},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4872209131717682},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4498245120048523},{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.44628968834877014},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.44037312269210815},{"id":"https://openalex.org/C81692654","wikidata":"https://www.wikidata.org/wiki/Q225926","display_name":"Kriging","level":2,"score":0.4215683341026306},{"id":"https://openalex.org/C65965080","wikidata":"https://www.wikidata.org/wiki/Q1806885","display_name":"Latent variable model","level":3,"score":0.41836339235305786},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.4178752303123474},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3977309763431549},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","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/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C17744445","wikidata":"https://www.wikidata.org/wiki/Q36442","display_name":"Political science","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}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1145/3412815.3416894","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3412815.3416894","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2020 ACM-IMS on Foundations of Data Science Conference","raw_type":"proceedings-article"},{"id":"pmh:oai:scholarsphere.psu.edu:b1274110-848f-4f48-8f0b-1ed0b1bd327f","is_oa":true,"landing_page_url":"https://scholarsphere.psu.edu/resources/b1274110-848f-4f48-8f0b-1ed0b1bd327f","pdf_url":"https://scholarsphere.psu.edu/resources/cd882941-dd70-478e-9704-80ab882670a1/downloads/16832","source":{"id":"https://openalex.org/S4306401507","display_name":"ScholarSphere (Penn State Libraries)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I3130638595","host_organization_name":"Pangasinan State University","host_organization_lineage":["https://openalex.org/I3130638595"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Proceedings of the 2020 ACM-IMS on Foundations of Data Science Conference","raw_type":"Article"}],"best_oa_location":{"id":"pmh:oai:scholarsphere.psu.edu:b1274110-848f-4f48-8f0b-1ed0b1bd327f","is_oa":true,"landing_page_url":"https://scholarsphere.psu.edu/resources/b1274110-848f-4f48-8f0b-1ed0b1bd327f","pdf_url":"https://scholarsphere.psu.edu/resources/cd882941-dd70-478e-9704-80ab882670a1/downloads/16832","source":{"id":"https://openalex.org/S4306401507","display_name":"ScholarSphere (Penn State Libraries)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I3130638595","host_organization_name":"Pangasinan State University","host_organization_lineage":["https://openalex.org/I3130638595"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Proceedings of the 2020 ACM-IMS on Foundations of Data Science Conference","raw_type":"Article"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G1140435109","display_name":null,"funder_award_id":"1636795","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G2789588635","display_name":null,"funder_award_id":"NCATS","funder_id":"https://openalex.org/F4320332161","funder_display_name":"National Institutes of Health"},{"id":"https://openalex.org/G3526734248","display_name":null,"funder_award_id":"1640834","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G407121706","display_name":null,"funder_award_id":"UL1 TR002014","funder_id":"https://openalex.org/F4320337373","funder_display_name":"Center for Information Technology"},{"id":"https://openalex.org/G4906924687","display_name":null,"funder_award_id":"TR002014","funder_id":"https://openalex.org/F4320337472","funder_display_name":"National Center for Advancing Translational Sciences"},{"id":"https://openalex.org/G5479771300","display_name":null,"funder_award_id":"UL1 TR002014","funder_id":"https://openalex.org/F4320337472","funder_display_name":"National Center for Advancing Translational Sciences"},{"id":"https://openalex.org/G5558081089","display_name":null,"funder_award_id":"UL1 TR002","funder_id":"https://openalex.org/F4320332161","funder_display_name":"National Institutes of Health"},{"id":"https://openalex.org/G6190180749","display_name":null,"funder_award_id":"UL1 TR002014","funder_id":"https://openalex.org/F4320332161","funder_display_name":"National Institutes of Health"},{"id":"https://openalex.org/G7184555466","display_name":null,"funder_award_id":"UL1 TR002014","funder_id":"https://openalex.org/F4320310419","funder_display_name":"Pennsylvania State University"},{"id":"https://openalex.org/G7544350372","display_name":null,"funder_award_id":"TR002014","funder_id":"https://openalex.org/F4320332161","funder_display_name":"National Institutes of Health"},{"id":"https://openalex.org/G848032724","display_name":null,"funder_award_id":"Science","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"},{"id":"https://openalex.org/F4320309370","display_name":"University of Pennsylvania","ror":"https://ror.org/00b30xv10"},{"id":"https://openalex.org/F4320310071","display_name":"Indian Institute of Science","ror":"https://ror.org/04dese585"},{"id":"https://openalex.org/F4320310419","display_name":"Pennsylvania State University","ror":"https://ror.org/04p491231"},{"id":"https://openalex.org/F4320332161","display_name":"National Institutes of Health","ror":"https://ror.org/01cwqze88"},{"id":"https://openalex.org/F4320334247","display_name":"Center for Big Data Analytics, University of Texas at Austin","ror":null},{"id":"https://openalex.org/F4320337373","display_name":"Center for Information Technology","ror":"https://ror.org/03jh5a977"},{"id":"https://openalex.org/F4320337472","display_name":"National Center for Advancing Translational Sciences","ror":"https://ror.org/04pw6fb54"}],"has_content":{"pdf":true,"grobid_xml":false},"content_urls":{"pdf":"https://content.openalex.org/works/W3094624470.pdf"},"referenced_works_count":34,"referenced_works":["https://openalex.org/W66306528","https://openalex.org/W137285897","https://openalex.org/W1492075988","https://openalex.org/W1571870753","https://openalex.org/W1585754671","https://openalex.org/W1895131631","https://openalex.org/W1895281781","https://openalex.org/W1919216911","https://openalex.org/W2004277545","https://openalex.org/W2007864935","https://openalex.org/W2024440126","https://openalex.org/W2036084078","https://openalex.org/W2086002623","https://openalex.org/W2093541024","https://openalex.org/W2104533781","https://openalex.org/W2119595900","https://openalex.org/W2124609748","https://openalex.org/W2129467541","https://openalex.org/W2132191455","https://openalex.org/W2143672530","https://openalex.org/W2149842772","https://openalex.org/W2166063021","https://openalex.org/W2167503371","https://openalex.org/W2169779569","https://openalex.org/W2278171434","https://openalex.org/W2343567063","https://openalex.org/W2500897968","https://openalex.org/W2553246783","https://openalex.org/W2597623762","https://openalex.org/W2963711523","https://openalex.org/W3005909817","https://openalex.org/W4211049957","https://openalex.org/W4300009529","https://openalex.org/W4300815417"],"related_works":["https://openalex.org/W2461917396","https://openalex.org/W1966667550","https://openalex.org/W2037497866","https://openalex.org/W4243467573","https://openalex.org/W566010457","https://openalex.org/W2600092203","https://openalex.org/W1502435251","https://openalex.org/W62001224","https://openalex.org/W3032390039","https://openalex.org/W1584341211"],"abstract_inverted_index":{"Many":[0],"real-world":[1],"applications":[2],"involve":[3],"longitudinal":[4,148,216],"data,":[5],"consisting":[6],"of":[7,9,15,36,47,55,79,93,111,118,122,132,143,171,177,179,206,211,225],"observations":[8,114,180,196],"several":[10],"variables,":[11],"where":[12],"different":[13],"subsets":[14],"variables":[16],"are":[17,74],"sampled":[18,81,147,213],"at":[19,115],"irregularly":[20,146,212],"spaced":[21],"time":[22,82],"points.":[23,83],"We":[24,84,127,161],"introduce":[25],"the":[26,37,56,69,72,80,91,100,112,119,123,130,133,140,189,194,199],"Longitudinal":[27],"Gaussian":[28,38,58,106],"Process":[29,39,59,107,135],"Latent":[30,40,60,158],"Variable":[31,41,61,159],"Model":[32,62],"(L-GPLVM),":[33],"a":[34,52,104,156,203,223],"variant":[35],"Model,":[42],"for":[43,109],"learning":[44,90,227],"compact":[45,209],"representations":[46,210],"such":[48],"data.":[49],"L-GPLVM":[50,94,184],"overcomes":[51],"key":[53],"limitation":[54],"Dynamic":[57],"and":[63,145,186,214,232],"its":[64],"variants,":[65],"which":[66],"rely":[67],"on":[68],"assumption":[70],"that":[71,181,183],"data":[73,149,166,173,201,217],"fully":[75],"observed":[76],"over":[77],"all":[78],"describe":[85],"an":[86],"effective":[87],"approach":[88],"to":[89,138,155,221],"parameters":[92],"from":[95],"sparse":[96,215],"observations,":[97],"by":[98],"coupling":[99],"dynamical":[101],"model":[102,108],"with":[103,150,164,174],"Multitask":[105],"sampling":[110],"missing":[113,195],"each":[116],"step":[117],"gradient-based":[120],"optimization":[121],"variational":[124],"lower":[125],"bound.":[126],"further":[128],"show":[129,182],"advantage":[131],"Sparse":[134],"Convolution":[136],"framework":[137],"learn":[139],"latent":[141],"representation":[142],"sparsely":[144],"minimal":[151],"computational":[152],"overhead":[153],"relative":[154],"standard":[157],"Model.":[160],"demonstrated":[162],"experiments":[163],"synthetic":[165],"as":[167,169],"well":[168],"variants":[170],"MOCAP":[172],"varying":[175],"degrees":[176],"sparsity":[178],"substantially":[185],"consistently":[187],"outperforms":[188],"state-of-the-art":[190],"alternatives":[191],"in":[192],"recovering":[193],"even":[197],"when":[198],"available":[200],"exhibits":[202],"high":[204],"degree":[205],"sparsity.":[207],"The":[208],"can":[218],"be":[219],"used":[220],"perform":[222],"variety":[224],"machine":[226],"tasks,":[228],"including":[229],"clustering,":[230],"classification,":[231],"regression.":[233]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2023,"cited_by_count":1},{"year":2021,"cited_by_count":2}],"updated_date":"2026-03-20T23:20:44.827607","created_date":"2025-10-10T00:00:00"}