{"id":"https://openalex.org/W2587279402","doi":"https://doi.org/10.1137/15m1053566","title":"Kernel Density Estimation on Spaces of Gaussian Distributions and Symmetric Positive Definite Matrices","display_name":"Kernel Density Estimation on Spaces of Gaussian Distributions and Symmetric Positive Definite Matrices","publication_year":2017,"publication_date":"2017-01-01","ids":{"openalex":"https://openalex.org/W2587279402","doi":"https://doi.org/10.1137/15m1053566","mag":"2587279402"},"language":"en","primary_location":{"id":"doi:10.1137/15m1053566","is_oa":false,"landing_page_url":"https://doi.org/10.1137/15m1053566","pdf_url":null,"source":{"id":"https://openalex.org/S152600803","display_name":"SIAM Journal on Imaging Sciences","issn_l":"1936-4954","issn":["1936-4954"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320508","host_organization_name":"Society for Industrial and Applied Mathematics","host_organization_lineage":["https://openalex.org/P4310320508"],"host_organization_lineage_names":["Society for Industrial and Applied Mathematics"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"SIAM Journal on Imaging Sciences","raw_type":"journal-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://hal.science/hal-01535731","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5059574332","display_name":"Emmanuel Chevallier","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Emmanuel Chevallier","raw_affiliation_strings":["CMM - Centre de Morphologie Math\u00e9matique (35 rue Saint-Honor\u00e9 77305 Fontainebleau cedex - France)"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"CMM - Centre de Morphologie Math\u00e9matique (35 rue Saint-Honor\u00e9 77305 Fontainebleau cedex - France)","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5039309709","display_name":"Emmanuel Kalunga","orcid":"https://orcid.org/0000-0001-7522-5253"},"institutions":[{"id":"https://openalex.org/I4390039377","display_name":"Laboratoire d'Ing\u00e9nierie des Syst\u00e8mes de Versailles","ror":"https://ror.org/01hcc6p91","country_code":"FR","type":"facility","lineage":["https://openalex.org/I195731000","https://openalex.org/I277688954","https://openalex.org/I4390039377"]}],"countries":["FR"],"is_corresponding":false,"raw_author_name":"Emmanuel Kalunga","raw_affiliation_strings":["LISV - Laboratoire d'Ing\u00e9nierie des Syst\u00e8mes de Versailles (B\u00e2timent Boucher\r\nP\u00f4le scientifique et technologique de V\u00e9lizy\r\n10-12 avenue de l'Europe\r\n78140 V\u00e9lizy\r\nT\u00e9l. : 01 39 25 49 65 - France)"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"LISV - Laboratoire d'Ing\u00e9nierie des Syst\u00e8mes de Versailles (B\u00e2timent Boucher\r\nP\u00f4le scientifique et technologique de V\u00e9lizy\r\n10-12 avenue de l'Europe\r\n78140 V\u00e9lizy\r\nT\u00e9l. : 01 39 25 49 65 - France)","institution_ids":["https://openalex.org/I4390039377"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5103530980","display_name":"Jes\u03cds Angulo","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Jes\u00fas Angulo","raw_affiliation_strings":["CMM - Centre de Morphologie Math\u00e9matique (35 rue Saint-Honor\u00e9 77305 Fontainebleau cedex - France)"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"CMM - Centre de Morphologie Math\u00e9matique (35 rue Saint-Honor\u00e9 77305 Fontainebleau cedex - France)","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":2.5153,"has_fulltext":false,"cited_by_count":23,"citation_normalized_percentile":{"value":0.88527644,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":98},"biblio":{"volume":"10","issue":"1","first_page":"191","last_page":"215"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11304","display_name":"Advanced Neuroimaging Techniques and Applications","score":0.996999979019165,"subfield":{"id":"https://openalex.org/subfields/2741","display_name":"Radiology, Nuclear Medicine and Imaging"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},"topics":[{"id":"https://openalex.org/T11304","display_name":"Advanced Neuroimaging Techniques and Applications","score":0.996999979019165,"subfield":{"id":"https://openalex.org/subfields/2741","display_name":"Radiology, Nuclear Medicine and Imaging"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},{"id":"https://openalex.org/T11210","display_name":"Mathematical Analysis and Transform Methods","score":0.9940000176429749,"subfield":{"id":"https://openalex.org/subfields/2604","display_name":"Applied Mathematics"},"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/T10688","display_name":"Image and Signal Denoising Methods","score":0.9869999885559082,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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/mathematics","display_name":"Mathematics","score":0.7525449991226196},{"id":"https://openalex.org/keywords/multivariate-kernel-density-estimation","display_name":"Multivariate kernel density estimation","score":0.7291556596755981},{"id":"https://openalex.org/keywords/kernel-density-estimation","display_name":"Kernel density estimation","score":0.7201098203659058},{"id":"https://openalex.org/keywords/univariate","display_name":"Univariate","score":0.7141863703727722},{"id":"https://openalex.org/keywords/kernel","display_name":"Kernel (algebra)","score":0.6772316098213196},{"id":"https://openalex.org/keywords/metric","display_name":"Metric (unit)","score":0.6359767317771912},{"id":"https://openalex.org/keywords/multivariate-statistics","display_name":"Multivariate statistics","score":0.5669199824333191},{"id":"https://openalex.org/keywords/gaussian","display_name":"Gaussian","score":0.5633997917175293},{"id":"https://openalex.org/keywords/density-estimation","display_name":"Density estimation","score":0.5592237710952759},{"id":"https://openalex.org/keywords/variable-kernel-density-estimation","display_name":"Variable kernel density estimation","score":0.5435568690299988},{"id":"https://openalex.org/keywords/multivariate-normal-distribution","display_name":"Multivariate normal distribution","score":0.5192820429801941},{"id":"https://openalex.org/keywords/positive-definite-matrix","display_name":"Positive-definite matrix","score":0.4938074052333832},{"id":"https://openalex.org/keywords/gaussian-function","display_name":"Gaussian function","score":0.4537891745567322},{"id":"https://openalex.org/keywords/applied-mathematics","display_name":"Applied mathematics","score":0.40375739336013794},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.353748619556427},{"id":"https://openalex.org/keywords/kernel-method","display_name":"Kernel method","score":0.25944966077804565},{"id":"https://openalex.org/keywords/pure-mathematics","display_name":"Pure mathematics","score":0.24775806069374084},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.22970610857009888},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.1307559609413147},{"id":"https://openalex.org/keywords/physics","display_name":"Physics","score":0.08772090077400208},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.07388260960578918}],"concepts":[{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.7525449991226196},{"id":"https://openalex.org/C84894716","wikidata":"https://www.wikidata.org/wiki/Q6935135","display_name":"Multivariate kernel density estimation","level":5,"score":0.7291556596755981},{"id":"https://openalex.org/C71134354","wikidata":"https://www.wikidata.org/wiki/Q458825","display_name":"Kernel density estimation","level":3,"score":0.7201098203659058},{"id":"https://openalex.org/C199163554","wikidata":"https://www.wikidata.org/wiki/Q1681619","display_name":"Univariate","level":3,"score":0.7141863703727722},{"id":"https://openalex.org/C74193536","wikidata":"https://www.wikidata.org/wiki/Q574844","display_name":"Kernel (algebra)","level":2,"score":0.6772316098213196},{"id":"https://openalex.org/C176217482","wikidata":"https://www.wikidata.org/wiki/Q860554","display_name":"Metric (unit)","level":2,"score":0.6359767317771912},{"id":"https://openalex.org/C161584116","wikidata":"https://www.wikidata.org/wiki/Q1952580","display_name":"Multivariate statistics","level":2,"score":0.5669199824333191},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.5633997917175293},{"id":"https://openalex.org/C189508267","wikidata":"https://www.wikidata.org/wiki/Q17088227","display_name":"Density estimation","level":3,"score":0.5592237710952759},{"id":"https://openalex.org/C195699287","wikidata":"https://www.wikidata.org/wiki/Q7915722","display_name":"Variable kernel density estimation","level":4,"score":0.5435568690299988},{"id":"https://openalex.org/C177384507","wikidata":"https://www.wikidata.org/wiki/Q1149000","display_name":"Multivariate normal distribution","level":3,"score":0.5192820429801941},{"id":"https://openalex.org/C49712288","wikidata":"https://www.wikidata.org/wiki/Q77601250","display_name":"Positive-definite matrix","level":3,"score":0.4938074052333832},{"id":"https://openalex.org/C7218915","wikidata":"https://www.wikidata.org/wiki/Q1054475","display_name":"Gaussian function","level":3,"score":0.4537891745567322},{"id":"https://openalex.org/C28826006","wikidata":"https://www.wikidata.org/wiki/Q33521","display_name":"Applied mathematics","level":1,"score":0.40375739336013794},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.353748619556427},{"id":"https://openalex.org/C122280245","wikidata":"https://www.wikidata.org/wiki/Q620622","display_name":"Kernel method","level":3,"score":0.25944966077804565},{"id":"https://openalex.org/C202444582","wikidata":"https://www.wikidata.org/wiki/Q837863","display_name":"Pure mathematics","level":1,"score":0.24775806069374084},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.22970610857009888},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.1307559609413147},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.08772090077400208},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.07388260960578918},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.0},{"id":"https://openalex.org/C158693339","wikidata":"https://www.wikidata.org/wiki/Q190524","display_name":"Eigenvalues and eigenvectors","level":2,"score":0.0},{"id":"https://openalex.org/C21547014","wikidata":"https://www.wikidata.org/wiki/Q1423657","display_name":"Operations management","level":1,"score":0.0},{"id":"https://openalex.org/C185429906","wikidata":"https://www.wikidata.org/wiki/Q1130160","display_name":"Estimator","level":2,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1137/15m1053566","is_oa":false,"landing_page_url":"https://doi.org/10.1137/15m1053566","pdf_url":null,"source":{"id":"https://openalex.org/S152600803","display_name":"SIAM Journal on Imaging Sciences","issn_l":"1936-4954","issn":["1936-4954"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320508","host_organization_name":"Society for Industrial and Applied Mathematics","host_organization_lineage":["https://openalex.org/P4310320508"],"host_organization_lineage_names":["Society for Industrial and Applied Mathematics"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"SIAM Journal on Imaging Sciences","raw_type":"journal-article"},{"id":"pmh:oai:HAL:hal-01535731v1","is_oa":true,"landing_page_url":"https://hal.science/hal-01535731","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,"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":"other-oa","license_id":"https://openalex.org/licenses/other-oa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"SIAM Journal on Imaging Sciences, 2017, 10 (1), pp.191 - 215. ⟨10.1137/15M1053566⟩","raw_type":"info:eu-repo/semantics/article"}],"best_oa_location":{"id":"pmh:oai:HAL:hal-01535731v1","is_oa":true,"landing_page_url":"https://hal.science/hal-01535731","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,"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":"other-oa","license_id":"https://openalex.org/licenses/other-oa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"SIAM Journal on Imaging Sciences, 2017, 10 (1), pp.191 - 215. ⟨10.1137/15M1053566⟩","raw_type":"info:eu-repo/semantics/article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":11,"referenced_works":["https://openalex.org/W1967632959","https://openalex.org/W1968333723","https://openalex.org/W1979502700","https://openalex.org/W1983496390","https://openalex.org/W1996955012","https://openalex.org/W2001056648","https://openalex.org/W2006134199","https://openalex.org/W2093015317","https://openalex.org/W2133323250","https://openalex.org/W3098817384","https://openalex.org/W3103171021"],"related_works":["https://openalex.org/W4241010850","https://openalex.org/W3212687977","https://openalex.org/W4251965284","https://openalex.org/W1834385407","https://openalex.org/W2355371556","https://openalex.org/W4239457139","https://openalex.org/W3123419490","https://openalex.org/W4386285810","https://openalex.org/W2144201579","https://openalex.org/W4320515934"],"abstract_inverted_index":{"This":[0],"paper":[1],"analyzes":[2],"the":[3,22,26,32,35],"kernel":[4],"density":[5,48],"estimation":[6,49],"on":[7,25],"spaces":[8],"of":[9,17,28,57],"Gaussian":[10],"distributions":[11],"endowed":[12],"with":[13],"different":[14],"metrics.":[15],"Expressions":[16],"kernels":[18,36],"are":[19,37],"provided":[20,38],"for":[21,40],"2-Wasserstein":[23],"metric":[24,34],"space":[27],"multivariate":[29,44],"Gaussians.":[30,46],"For":[31],"Fisher":[33],"only":[39],"univariate":[41],"Gaussians":[42],"and":[43],"centered":[45],"The":[47],"is":[50],"successfully":[51],"applied":[52],"to":[53],"a":[54],"classification":[55],"problem":[56],"electro-encephalographic":[58],"signals.":[59]},"counts_by_year":[{"year":2025,"cited_by_count":3},{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":3},{"year":2020,"cited_by_count":6},{"year":2019,"cited_by_count":1},{"year":2018,"cited_by_count":3},{"year":2017,"cited_by_count":3},{"year":2016,"cited_by_count":1},{"year":2015,"cited_by_count":1}],"updated_date":"2026-07-31T08:31:51.225901","created_date":"2025-10-10T00:00:00"}