{"id":"https://openalex.org/W2164018267","doi":"https://doi.org/10.1109/icmla.2004.1383523","title":"EMPRR: a high-dimensional EM-based peicewise regression algorithm","display_name":"EMPRR: a high-dimensional EM-based peicewise regression algorithm","publication_year":2005,"publication_date":"2005-04-06","ids":{"openalex":"https://openalex.org/W2164018267","doi":"https://doi.org/10.1109/icmla.2004.1383523","mag":"2164018267"},"language":"en","primary_location":{"id":"doi:10.1109/icmla.2004.1383523","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icmla.2004.1383523","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2004 International Conference on Machine Learning and Applications, 2004. Proceedings.","raw_type":"proceedings-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/A5058914957","display_name":"Manimozhiyan Arumugam","orcid":"https://orcid.org/0000-0002-0886-9101"},"institutions":[{"id":"https://openalex.org/I204465549","display_name":"Washington University in St. Louis","ror":"https://ror.org/01yc7t268","country_code":"US","type":"education","lineage":["https://openalex.org/I204465549"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"M. Arumugam","raw_affiliation_strings":["Department of Computer Science & Engineering, Washington University of Saint Louis, Saint Louis, MO, USA"],"affiliations":[{"raw_affiliation_string":"Department of Computer Science & Engineering, Washington University of Saint Louis, Saint Louis, MO, USA","institution_ids":["https://openalex.org/I204465549"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5002697978","display_name":"Stephen Scott","orcid":"https://orcid.org/0000-0001-5227-6875"},"institutions":[{"id":"https://openalex.org/I114395901","display_name":"University of Nebraska\u2013Lincoln","ror":"https://ror.org/043mer456","country_code":"US","type":"education","lineage":["https://openalex.org/I114395901"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"S.D. Scott","raw_affiliation_strings":["Department of Computer Science & Engineering, University of Nebraska, Lincolnshire, NE, USA"],"affiliations":[{"raw_affiliation_string":"Department of Computer Science & Engineering, University of Nebraska, Lincolnshire, NE, USA","institution_ids":["https://openalex.org/I114395901"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5058914957"],"corresponding_institution_ids":["https://openalex.org/I204465549"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":4,"citation_normalized_percentile":{"value":0.25067677,"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":"264","last_page":"271"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11309","display_name":"Music and Audio Processing","score":0.9908999800682068,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"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/T11309","display_name":"Music and Audio Processing","score":0.9908999800682068,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"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/T10538","display_name":"Data Mining Algorithms and Applications","score":0.9887999892234802,"subfield":{"id":"https://openalex.org/subfields/1710","display_name":"Information Systems"},"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/T12535","display_name":"Machine Learning and Data Classification","score":0.982200026512146,"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/benchmark","display_name":"Benchmark (surveying)","score":0.7985765933990479},{"id":"https://openalex.org/keywords/piecewise","display_name":"Piecewise","score":0.7496177554130554},{"id":"https://openalex.org/keywords/boundary","display_name":"Boundary (topology)","score":0.6049396395683289},{"id":"https://openalex.org/keywords/tree","display_name":"Tree (set theory)","score":0.5771123170852661},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.573035478591919},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.563639760017395},{"id":"https://openalex.org/keywords/regression","display_name":"Regression","score":0.5290542840957642},{"id":"https://openalex.org/keywords/segmented-regression","display_name":"Segmented regression","score":0.5152440667152405},{"id":"https://openalex.org/keywords/synthetic-data","display_name":"Synthetic data","score":0.49321550130844116},{"id":"https://openalex.org/keywords/data-modeling","display_name":"Data modeling","score":0.49000945687294006},{"id":"https://openalex.org/keywords/regression-analysis","display_name":"Regression analysis","score":0.46226656436920166},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.37222349643707275},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.347733736038208},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.2291591763496399},{"id":"https://openalex.org/keywords/polynomial-regression","display_name":"Polynomial regression","score":0.14389219880104065},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.12366902828216553}],"concepts":[{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.7985765933990479},{"id":"https://openalex.org/C164660894","wikidata":"https://www.wikidata.org/wiki/Q2037833","display_name":"Piecewise","level":2,"score":0.7496177554130554},{"id":"https://openalex.org/C62354387","wikidata":"https://www.wikidata.org/wiki/Q875399","display_name":"Boundary (topology)","level":2,"score":0.6049396395683289},{"id":"https://openalex.org/C113174947","wikidata":"https://www.wikidata.org/wiki/Q2859736","display_name":"Tree (set theory)","level":2,"score":0.5771123170852661},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.573035478591919},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.563639760017395},{"id":"https://openalex.org/C83546350","wikidata":"https://www.wikidata.org/wiki/Q1139051","display_name":"Regression","level":2,"score":0.5290542840957642},{"id":"https://openalex.org/C35519122","wikidata":"https://www.wikidata.org/wiki/Q3775699","display_name":"Segmented regression","level":4,"score":0.5152440667152405},{"id":"https://openalex.org/C160920958","wikidata":"https://www.wikidata.org/wiki/Q7662746","display_name":"Synthetic data","level":2,"score":0.49321550130844116},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.49000945687294006},{"id":"https://openalex.org/C152877465","wikidata":"https://www.wikidata.org/wiki/Q208042","display_name":"Regression analysis","level":2,"score":0.46226656436920166},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.37222349643707275},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.347733736038208},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.2291591763496399},{"id":"https://openalex.org/C120068334","wikidata":"https://www.wikidata.org/wiki/Q45343","display_name":"Polynomial regression","level":3,"score":0.14389219880104065},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.12366902828216553},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.0},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0},{"id":"https://openalex.org/C13280743","wikidata":"https://www.wikidata.org/wiki/Q131089","display_name":"Geodesy","level":1,"score":0.0},{"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:10.1109/icmla.2004.1383523","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icmla.2004.1383523","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2004 International Conference on Machine Learning and Applications, 2004. Proceedings.","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.4699999988079071,"display_name":"Peace, Justice and strong institutions","id":"https://metadata.un.org/sdg/16"}],"awards":[],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"},{"id":"https://openalex.org/F4320332161","display_name":"National Institutes of Health","ror":"https://ror.org/01cwqze88"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":23,"referenced_works":["https://openalex.org/W1504694836","https://openalex.org/W1560724230","https://openalex.org/W1594031697","https://openalex.org/W1781787795","https://openalex.org/W1965666958","https://openalex.org/W1985810193","https://openalex.org/W2049633694","https://openalex.org/W2051589410","https://openalex.org/W2052515148","https://openalex.org/W2087070363","https://openalex.org/W2090712000","https://openalex.org/W2102201073","https://openalex.org/W2123504579","https://openalex.org/W2124470530","https://openalex.org/W2125055259","https://openalex.org/W2159038093","https://openalex.org/W2170120409","https://openalex.org/W2256578114","https://openalex.org/W2528357704","https://openalex.org/W2912391306","https://openalex.org/W3144619878","https://openalex.org/W4233487108","https://openalex.org/W4240696639"],"related_works":["https://openalex.org/W4245818917","https://openalex.org/W2055707752","https://openalex.org/W3099761985","https://openalex.org/W2276242003","https://openalex.org/W2528235544","https://openalex.org/W3205722037","https://openalex.org/W2067336082","https://openalex.org/W4226182374","https://openalex.org/W2092886441","https://openalex.org/W4200634021"],"abstract_inverted_index":{"We":[0,26,41],"propose":[1],"a":[2],"novel":[3],"general":[4],"piecewise":[5,60],"surface":[6],"regression":[7,54],"model":[8,48],"that":[9,36,69],"allows":[10],"for":[11,32],"arbitrary":[12,21],"functions":[13],"to":[14,38],"be":[15],"used":[16],"in":[17],"each":[18],"piece,":[19],"and":[20,50,62,81,92],"boundary":[22],"swfaces":[23],"between":[24],"pieces.":[25],"also":[27],"give":[28],"an":[29],"EM-based":[30],"algorithm":[31],"this":[33],"model,":[34],"EMPRR,":[35],"scales":[37],"high":[39],"dimensions.":[40],"compare":[42],"EMPRR's":[43],"performance":[44],"with":[45],"those":[46],"of":[47],"trees":[49],"functional":[51],"trees,":[52],"two":[53,74],"tree":[55],"learning":[56],"methods,":[57],"on":[58,76,84],"synthetic":[59,78],"data":[61,64,79,87],"benchmark":[63,86],"sets.":[65],"Our":[66],"results":[67],"show":[68],"EMPRR":[70],"outperforms":[71],"the":[72,77,85],"other":[73],"methods":[75],"sets":[80,88],"performs":[82],"competitively":[83],"while":[89],"generating":[90],"accurate":[91],"compact":[93],"models.":[94]},"counts_by_year":[{"year":2017,"cited_by_count":1},{"year":2015,"cited_by_count":1},{"year":2012,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}