{"id":"https://openalex.org/W4210899361","doi":"https://doi.org/10.1109/access.2022.3149280","title":"Efficient Density Estimation for High-Dimensional Data","display_name":"Efficient Density Estimation for High-Dimensional Data","publication_year":2022,"publication_date":"2022-01-01","ids":{"openalex":"https://openalex.org/W4210899361","doi":"https://doi.org/10.1109/access.2022.3149280"},"language":"en","primary_location":{"id":"doi:10.1109/access.2022.3149280","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2022.3149280","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/9668973/09705555.pdf","source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://ieeexplore.ieee.org/ielx7/6287639/9668973/09705555.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5077976574","display_name":"Aref Majdara","orcid":"https://orcid.org/0000-0003-0807-6731"},"institutions":[{"id":"https://openalex.org/I11957088","display_name":"Michigan Technological University","ror":"https://ror.org/0036rpn28","country_code":"US","type":"education","lineage":["https://openalex.org/I11957088"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Aref Majdara","raw_affiliation_strings":["Department of Electrical and Computer Engineering, Michigan Technological University, Houghton, MI, USA"],"raw_orcid":"https://orcid.org/0000-0003-0807-6731","affiliations":[{"raw_affiliation_string":"Department of Electrical and Computer Engineering, Michigan Technological University, Houghton, MI, USA","institution_ids":["https://openalex.org/I11957088"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5004862330","display_name":"Saeid Nooshabadi","orcid":"https://orcid.org/0000-0001-8596-1350"},"institutions":[{"id":"https://openalex.org/I11957088","display_name":"Michigan Technological University","ror":"https://ror.org/0036rpn28","country_code":"US","type":"education","lineage":["https://openalex.org/I11957088"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Saeid Nooshabadi","raw_affiliation_strings":["Department of Electrical and Computer Engineering, Michigan Technological University, Houghton, MI, USA"],"raw_orcid":"https://orcid.org/0000-0001-8596-1350","affiliations":[{"raw_affiliation_string":"Department of Electrical and Computer Engineering, Michigan Technological University, Houghton, MI, USA","institution_ids":["https://openalex.org/I11957088"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":{"value":1850,"currency":"USD","value_usd":1850},"apc_paid":{"value":1850,"currency":"USD","value_usd":1850},"fwci":0.1387,"has_fulltext":true,"cited_by_count":2,"citation_normalized_percentile":{"value":0.51334583,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":98},"biblio":{"volume":"10","issue":null,"first_page":"16592","last_page":"16608"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11901","display_name":"Bayesian Methods and Mixture Models","score":0.9955000281333923,"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":0.9955000281333923,"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/T12814","display_name":"Gaussian Processes and Bayesian Inference","score":0.994700014591217,"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/T10320","display_name":"Neural Networks and Applications","score":0.9926000237464905,"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/computer-science","display_name":"Computer science","score":0.7795265913009644},{"id":"https://openalex.org/keywords/density-estimation","display_name":"Density estimation","score":0.7044397592544556},{"id":"https://openalex.org/keywords/multivariate-kernel-density-estimation","display_name":"Multivariate kernel density estimation","score":0.6700832843780518},{"id":"https://openalex.org/keywords/copula","display_name":"Copula (linguistics)","score":0.5670885443687439},{"id":"https://openalex.org/keywords/kernel-density-estimation","display_name":"Kernel density estimation","score":0.5393718481063843},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.4898952841758728},{"id":"https://openalex.org/keywords/rendering","display_name":"Rendering (computer graphics)","score":0.48936671018600464},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.4381735324859619},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.37633076310157776},{"id":"https://openalex.org/keywords/kernel-method","display_name":"Kernel method","score":0.3264811336994171},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.2501814365386963},{"id":"https://openalex.org/keywords/variable-kernel-density-estimation","display_name":"Variable kernel density estimation","score":0.2102280557155609},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.13748663663864136},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.13141483068466187},{"id":"https://openalex.org/keywords/estimator","display_name":"Estimator","score":0.08826383948326111}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7795265913009644},{"id":"https://openalex.org/C189508267","wikidata":"https://www.wikidata.org/wiki/Q17088227","display_name":"Density estimation","level":3,"score":0.7044397592544556},{"id":"https://openalex.org/C84894716","wikidata":"https://www.wikidata.org/wiki/Q6935135","display_name":"Multivariate kernel density estimation","level":5,"score":0.6700832843780518},{"id":"https://openalex.org/C17618745","wikidata":"https://www.wikidata.org/wiki/Q207509","display_name":"Copula (linguistics)","level":2,"score":0.5670885443687439},{"id":"https://openalex.org/C71134354","wikidata":"https://www.wikidata.org/wiki/Q458825","display_name":"Kernel density estimation","level":3,"score":0.5393718481063843},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.4898952841758728},{"id":"https://openalex.org/C205711294","wikidata":"https://www.wikidata.org/wiki/Q176953","display_name":"Rendering (computer graphics)","level":2,"score":0.48936671018600464},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.4381735324859619},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.37633076310157776},{"id":"https://openalex.org/C122280245","wikidata":"https://www.wikidata.org/wiki/Q620622","display_name":"Kernel method","level":3,"score":0.3264811336994171},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.2501814365386963},{"id":"https://openalex.org/C195699287","wikidata":"https://www.wikidata.org/wiki/Q7915722","display_name":"Variable kernel density estimation","level":4,"score":0.2102280557155609},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.13748663663864136},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.13141483068466187},{"id":"https://openalex.org/C185429906","wikidata":"https://www.wikidata.org/wiki/Q1130160","display_name":"Estimator","level":2,"score":0.08826383948326111},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.0},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/access.2022.3149280","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2022.3149280","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/9668973/09705555.pdf","source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:d5d4a4444fe54b6087fac6c2671357ca","is_oa":true,"landing_page_url":"https://doaj.org/article/d5d4a4444fe54b6087fac6c2671357ca","pdf_url":null,"source":{"id":"https://openalex.org/S4306401280","display_name":"DOAJ (DOAJ: Directory of Open Access Journals)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"IEEE Access, Vol 10, Pp 16592-16608 (2022)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.1109/access.2022.3149280","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2022.3149280","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/9668973/09705555.pdf","source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4210899361.pdf","grobid_xml":"https://content.openalex.org/works/W4210899361.grobid-xml"},"referenced_works_count":48,"referenced_works":["https://openalex.org/W596018869","https://openalex.org/W1531520279","https://openalex.org/W1551760581","https://openalex.org/W1558646600","https://openalex.org/W1981783308","https://openalex.org/W2021655156","https://openalex.org/W2046914656","https://openalex.org/W2058852146","https://openalex.org/W2068688638","https://openalex.org/W2069739265","https://openalex.org/W2097998671","https://openalex.org/W2108447811","https://openalex.org/W2124554697","https://openalex.org/W2128889510","https://openalex.org/W2137824508","https://openalex.org/W2159075009","https://openalex.org/W2164132908","https://openalex.org/W2171585891","https://openalex.org/W2242440501","https://openalex.org/W2373618835","https://openalex.org/W2415715200","https://openalex.org/W2492307518","https://openalex.org/W2528149095","https://openalex.org/W2533200457","https://openalex.org/W2778939515","https://openalex.org/W2897534840","https://openalex.org/W2914359216","https://openalex.org/W2930329231","https://openalex.org/W2963039126","https://openalex.org/W2963595502","https://openalex.org/W2963647337","https://openalex.org/W2979368289","https://openalex.org/W3016797103","https://openalex.org/W3104489269","https://openalex.org/W3122448711","https://openalex.org/W3194404910","https://openalex.org/W3200768790","https://openalex.org/W4213251304","https://openalex.org/W4235667863","https://openalex.org/W4241717233","https://openalex.org/W4248721357","https://openalex.org/W4250857377","https://openalex.org/W4251232855","https://openalex.org/W4300692961","https://openalex.org/W4388290870","https://openalex.org/W6633344870","https://openalex.org/W6690359062","https://openalex.org/W6708990349"],"related_works":["https://openalex.org/W1482149696","https://openalex.org/W2115980193","https://openalex.org/W2026307144","https://openalex.org/W4241010850","https://openalex.org/W2583877436","https://openalex.org/W3212687977","https://openalex.org/W2977337932","https://openalex.org/W2144201579","https://openalex.org/W3123419490","https://openalex.org/W2776263260"],"abstract_inverted_index":{"Multivariate":[0],"density":[1,24,74,90,99],"estimation":[2,25,75,91,100],"methods":[3],"typically":[4],"work":[5],"well":[6],"in":[7,16,26,73,79,94],"low":[8],"dimensions":[9,18],"and":[10,61,69,97,106,127],"their":[11],"extension":[12],"to":[13],"data":[14,29,67,77,85,104],"analytics":[15,78],"high":[17,80],"domain":[19],"has":[20],"proven":[21],"challenging.":[22],"For":[23],"high-dimensional":[27],"big":[28],"domains,":[30],"the":[31,44,83,89],"non-parametric":[32],"Bayesian":[33,49],"sequential":[34],"partitioning":[35,43],"(BSP)":[36],"algorithm":[37,70,107],"provides":[38],"an":[39],"efficient":[40,65,113],"way":[41],"of":[42,59,119],"sample":[45],"space,":[46],"based":[47],"on":[48],"inference.":[50],"In":[51],"this":[52],"paper,":[53],"we":[54,87],"present":[55],"a":[56,63],"detailed":[57],"analysis":[58],"BSP":[60,96],"provide":[62],"computationally":[64],"copula-transformed":[66,84],"structure":[68],"for":[71,76,92,111],"use":[72],"dimensions.":[81],"Using":[82],"structure,":[86],"implement":[88],"marginals":[93],"both":[95],"kernel":[98],"(KDE)":[101],"methods.":[102],"The":[103],"structures":[105],"are":[108],"suitably":[109],"designed":[110],"most":[112],"rendering":[114],"into":[115],"parallel":[116],"processing":[117],"paradigms":[118],"open":[120],"multi-processing":[121],"(OPENMP":[122],"<sup":[123],"xmlns:mml=\"http://www.w3.org/1998/Math/MathML\"":[124],"xmlns:xlink=\"http://www.w3.org/1999/xlink\">\u00ae</sup>":[125],")":[126],"message":[128],"passing":[129],"interface":[130],"(MPI).":[131]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2023,"cited_by_count":1}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}
