{"id":"https://openalex.org/W2952908039","doi":"https://doi.org/10.1145/3292500.3330692","title":"Nonparametric Mixture of Sparse Regressions on Spatio-Temporal Data -- An Application to Climate Prediction","display_name":"Nonparametric Mixture of Sparse Regressions on Spatio-Temporal Data -- An Application to Climate Prediction","publication_year":2019,"publication_date":"2019-07-25","ids":{"openalex":"https://openalex.org/W2952908039","doi":"https://doi.org/10.1145/3292500.3330692","mag":"2952908039"},"language":"en","primary_location":{"id":"doi:10.1145/3292500.3330692","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3292500.3330692","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3292500.3330692","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery &amp; Data Mining","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://dl.acm.org/doi/pdf/10.1145/3292500.3330692","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5100671636","display_name":"Yumin Liu","orcid":"https://orcid.org/0000-0001-5978-8953"},"institutions":[{"id":"https://openalex.org/I12912129","display_name":"Northeastern University","ror":"https://ror.org/04t5xt781","country_code":"US","type":"education","lineage":["https://openalex.org/I12912129"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Yumin Liu","raw_affiliation_strings":["Northeastern University, Boston, MA, USA"],"affiliations":[{"raw_affiliation_string":"Northeastern University, Boston, MA, USA","institution_ids":["https://openalex.org/I12912129"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5091082342","display_name":"Junxiang Chen","orcid":"https://orcid.org/0000-0002-8897-754X"},"institutions":[{"id":"https://openalex.org/I12912129","display_name":"Northeastern University","ror":"https://ror.org/04t5xt781","country_code":"US","type":"education","lineage":["https://openalex.org/I12912129"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Junxiang Chen","raw_affiliation_strings":["Northeastern University, Boston, MA, USA"],"affiliations":[{"raw_affiliation_string":"Northeastern University, Boston, MA, USA","institution_ids":["https://openalex.org/I12912129"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5064658255","display_name":"Auroop R. Ganguly","orcid":"https://orcid.org/0000-0002-4292-4856"},"institutions":[{"id":"https://openalex.org/I12912129","display_name":"Northeastern University","ror":"https://ror.org/04t5xt781","country_code":"US","type":"education","lineage":["https://openalex.org/I12912129"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Auroop Ganguly","raw_affiliation_strings":["Northeastern University, Boston, MA, USA"],"affiliations":[{"raw_affiliation_string":"Northeastern University, Boston, MA, USA","institution_ids":["https://openalex.org/I12912129"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5042038501","display_name":"Jennifer Dy","orcid":"https://orcid.org/0000-0002-8430-134X"},"institutions":[{"id":"https://openalex.org/I12912129","display_name":"Northeastern University","ror":"https://ror.org/04t5xt781","country_code":"US","type":"education","lineage":["https://openalex.org/I12912129"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Jennifer Dy","raw_affiliation_strings":["Northeastern University, Boston, MA, USA"],"affiliations":[{"raw_affiliation_string":"Northeastern University, Boston, MA, USA","institution_ids":["https://openalex.org/I12912129"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5100671636"],"corresponding_institution_ids":["https://openalex.org/I12912129"],"apc_list":null,"apc_paid":null,"fwci":0.289,"has_fulltext":true,"cited_by_count":3,"citation_normalized_percentile":{"value":0.65854481,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":94},"biblio":{"volume":null,"issue":null,"first_page":"2556","last_page":"2564"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11901","display_name":"Bayesian Methods and Mixture Models","score":0.9976000189781189,"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":0.9976000189781189,"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/T10064","display_name":"Complex Network Analysis Techniques","score":0.996399998664856,"subfield":{"id":"https://openalex.org/subfields/3109","display_name":"Statistical and Nonlinear Physics"},"field":{"id":"https://openalex.org/fields/31","display_name":"Physics and Astronomy"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10637","display_name":"Advanced Clustering Algorithms Research","score":0.9907000064849854,"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/nonparametric-statistics","display_name":"Nonparametric statistics","score":0.6418427228927612},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5645012855529785},{"id":"https://openalex.org/keywords/regression","display_name":"Regression","score":0.4547012746334076},{"id":"https://openalex.org/keywords/econometrics","display_name":"Econometrics","score":0.4494216740131378},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.4396183490753174},{"id":"https://openalex.org/keywords/data-modeling","display_name":"Data modeling","score":0.4248526096343994},{"id":"https://openalex.org/keywords/nonparametric-regression","display_name":"Nonparametric regression","score":0.4191013276576996},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4049844443798065},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.38045522570610046},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3729397654533386},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.365009605884552},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.2594980299472809}],"concepts":[{"id":"https://openalex.org/C102366305","wikidata":"https://www.wikidata.org/wiki/Q1097688","display_name":"Nonparametric statistics","level":2,"score":0.6418427228927612},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5645012855529785},{"id":"https://openalex.org/C83546350","wikidata":"https://www.wikidata.org/wiki/Q1139051","display_name":"Regression","level":2,"score":0.4547012746334076},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.4494216740131378},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4396183490753174},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.4248526096343994},{"id":"https://openalex.org/C74127309","wikidata":"https://www.wikidata.org/wiki/Q3455886","display_name":"Nonparametric regression","level":3,"score":0.4191013276576996},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4049844443798065},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.38045522570610046},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3729397654533386},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.365009605884552},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.2594980299472809},{"id":"https://openalex.org/C77088390","wikidata":"https://www.wikidata.org/wiki/Q8513","display_name":"Database","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3292500.3330692","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3292500.3330692","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3292500.3330692","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery &amp; Data Mining","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.1145/3292500.3330692","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3292500.3330692","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3292500.3330692","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery &amp; Data Mining","raw_type":"proceedings-article"},"sustainable_development_goals":[{"display_name":"Climate action","id":"https://metadata.un.org/sdg/13","score":0.8600000143051147}],"awards":[{"id":"https://openalex.org/G2137674310","display_name":null,"funder_award_id":"CCF-1442728","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G4593386230","display_name":null,"funder_award_id":"NSF CCF-1442728","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G7314811336","display_name":null,"funder_award_id":"1442728","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/F4320308380","display_name":"Yale University","ror":"https://ror.org/03v76x132"}],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W2952908039.pdf","grobid_xml":"https://content.openalex.org/works/W2952908039.grobid-xml"},"referenced_works_count":43,"referenced_works":["https://openalex.org/W33832734","https://openalex.org/W35490052","https://openalex.org/W125703900","https://openalex.org/W263845233","https://openalex.org/W1601139921","https://openalex.org/W1805172955","https://openalex.org/W1989946722","https://openalex.org/W1994102001","https://openalex.org/W1999733221","https://openalex.org/W1999974018","https://openalex.org/W2001572836","https://openalex.org/W2007069447","https://openalex.org/W2024966118","https://openalex.org/W2029730867","https://openalex.org/W2047635155","https://openalex.org/W2058551761","https://openalex.org/W2060347661","https://openalex.org/W2090658414","https://openalex.org/W2095396557","https://openalex.org/W2097645701","https://openalex.org/W2100163972","https://openalex.org/W2113988163","https://openalex.org/W2120618726","https://openalex.org/W2120653676","https://openalex.org/W2130681737","https://openalex.org/W2139688922","https://openalex.org/W2147880316","https://openalex.org/W2163738067","https://openalex.org/W2166974961","https://openalex.org/W2529832773","https://openalex.org/W2575823240","https://openalex.org/W2763683794","https://openalex.org/W2811423591","https://openalex.org/W2913323966","https://openalex.org/W2939474406","https://openalex.org/W2974527409","https://openalex.org/W3101035442","https://openalex.org/W3105945687","https://openalex.org/W3121709681","https://openalex.org/W3145164839","https://openalex.org/W4233343470","https://openalex.org/W4285719527","https://openalex.org/W4399360124"],"related_works":["https://openalex.org/W3122025248","https://openalex.org/W1500054897","https://openalex.org/W2330399684","https://openalex.org/W2076214177","https://openalex.org/W1996121315","https://openalex.org/W2105228693","https://openalex.org/W2155106029","https://openalex.org/W2109785413","https://openalex.org/W1505751153","https://openalex.org/W3122882238"],"abstract_inverted_index":{"Climate":[0],"prediction":[1,38,115],"is":[2],"a":[3,63,70,107,121],"very":[4],"challenging":[5],"problem.":[6],"Many":[7],"institutes":[8],"around":[9],"the":[10,34,53,94],"world":[11],"try":[12],"to":[13,91,101],"predict":[14],"climate":[15,19,143],"variables":[16],"by":[17,52],"building":[18],"models":[20],"called":[21],"General":[22],"Circulation":[23],"Models":[24],"(GCMs),":[25],"which":[26,57],"are":[27,59,112,136],"based":[28],"on":[29],"mathematical":[30],"equations":[31],"that":[32,111],"describe":[33],"physical":[35],"processes.":[36],"The":[37],"abilities":[39],"of":[40,55,78,96,109],"different":[41,47],"GCMs":[42,58,110],"may":[43],"vary":[44],"dramatically":[45],"across":[46],"regions":[48],"and":[49,66,105,141],"time.":[50],"Motivated":[51],"need":[54],"identifying":[56],"more":[60],"useful":[61,113],"for":[62,114,131,138],"particular":[64],"region":[65],"time,":[67],"we":[68],"introduce":[69],"clustering":[71],"model":[72,88],"combining":[73],"Dirichlet":[74],"Process":[75],"(DP)":[76],"mixture":[77],"sparse":[79],"linear":[80],"regression":[81],"with":[82,120],"Markov":[83],"Random":[84],"Fields":[85],"(MRFs).":[86],"This":[87],"incorporates":[89],"DP":[90],"automatically":[92],"determine":[93],"number":[95],"clusters,":[97],"imposes":[98],"MRF":[99],"constraints":[100],"guarantee":[102],"spatio-temporal":[103,118],"smoothness,":[104],"selects":[106],"subset":[108],"within":[116],"each":[117],"cluster":[119],"spike-and-slab":[122],"prior.":[123],"We":[124],"derive":[125],"an":[126],"effective":[127],"Gibbs":[128],"sampling":[129],"method":[130],"this":[132],"model.":[133],"Experimental":[134],"results":[135],"provided":[137],"both":[139],"synthetic":[140],"real-world":[142],"data.":[144]},"counts_by_year":[{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":1}],"updated_date":"2026-04-21T08:09:41.155169","created_date":"2025-10-10T00:00:00"}
