{"id":"https://openalex.org/W4312419414","doi":"https://doi.org/10.1109/tsp.2022.3230336","title":"Robust Self-Tuning Semiparametric PCA for Contaminated Elliptical Distribution","display_name":"Robust Self-Tuning Semiparametric PCA for Contaminated Elliptical Distribution","publication_year":2022,"publication_date":"2022-01-01","ids":{"openalex":"https://openalex.org/W4312419414","doi":"https://doi.org/10.1109/tsp.2022.3230336"},"language":"en","primary_location":{"id":"doi:10.1109/tsp.2022.3230336","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tsp.2022.3230336","pdf_url":null,"source":{"id":"https://openalex.org/S168680287","display_name":"IEEE Transactions on Signal Processing","issn_l":"1053-587X","issn":["1053-587X","1941-0476"],"is_oa":false,"is_in_doaj":false,"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":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Signal Processing","raw_type":"journal-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/A5043868398","display_name":"Hung Hung","orcid":"https://orcid.org/0000-0003-4022-2081"},"institutions":[{"id":"https://openalex.org/I16733864","display_name":"National Taiwan University","ror":"https://ror.org/05bqach95","country_code":"TW","type":"education","lineage":["https://openalex.org/I16733864"]}],"countries":["TW"],"is_corresponding":true,"raw_author_name":"Hung Hung","raw_affiliation_strings":["Institute of Health Data Analytics and Statistics, National Taiwan University, Taipei, Taiwan"],"affiliations":[{"raw_affiliation_string":"Institute of Health Data Analytics and Statistics, National Taiwan University, Taipei, Taiwan","institution_ids":["https://openalex.org/I16733864"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5102806691","display_name":"Su\u2010Yun Huang","orcid":"https://orcid.org/0000-0002-1602-2832"},"institutions":[{"id":"https://openalex.org/I4210141710","display_name":"Institute of Statistical Science, Academia Sinica","ror":"https://ror.org/044gv5910","country_code":"TW","type":"facility","lineage":["https://openalex.org/I4210141710","https://openalex.org/I84653119"]}],"countries":["TW"],"is_corresponding":false,"raw_author_name":"Su-Yun Huang","raw_affiliation_strings":["Institute of Statistical Science, Academia Sinica, Taipei, Taiwan"],"affiliations":[{"raw_affiliation_string":"Institute of Statistical Science, Academia Sinica, Taipei, Taiwan","institution_ids":["https://openalex.org/I4210141710"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5060169206","display_name":"Shinto Eguchi","orcid":"https://orcid.org/0000-0002-3974-618X"},"institutions":[{"id":"https://openalex.org/I4210134673","display_name":"The Institute of Statistical Mathematics","ror":"https://ror.org/03jcejr58","country_code":"JP","type":"facility","lineage":["https://openalex.org/I1319490839","https://openalex.org/I4210134673","https://openalex.org/I4210158934"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Shinto Eguchi","raw_affiliation_strings":["Institute of Statistical Mathematics, Tokyo, Japan"],"affiliations":[{"raw_affiliation_string":"Institute of Statistical Mathematics, Tokyo, Japan","institution_ids":["https://openalex.org/I4210134673"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5043868398"],"corresponding_institution_ids":["https://openalex.org/I16733864"],"apc_list":null,"apc_paid":null,"fwci":0.2013,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":{"value":0.49277847,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":95,"max":99},"biblio":{"volume":"70","issue":null,"first_page":"5885","last_page":"5897"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10688","display_name":"Image and Signal Denoising Methods","score":0.9984999895095825,"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"}},"topics":[{"id":"https://openalex.org/T10688","display_name":"Image and Signal Denoising Methods","score":0.9984999895095825,"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"}},{"id":"https://openalex.org/T10640","display_name":"Spectroscopy and Chemometric Analyses","score":0.9965000152587891,"subfield":{"id":"https://openalex.org/subfields/1602","display_name":"Analytical Chemistry"},"field":{"id":"https://openalex.org/fields/16","display_name":"Chemistry"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11871","display_name":"Advanced Statistical Methods and Models","score":0.9962999820709229,"subfield":{"id":"https://openalex.org/subfields/2613","display_name":"Statistics and Probability"},"field":{"id":"https://openalex.org/fields/26","display_name":"Mathematics"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.4589380621910095},{"id":"https://openalex.org/keywords/semiparametric-model","display_name":"Semiparametric model","score":0.45814746618270874},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.4576714038848877},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.3546797037124634},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.3389757573604584},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3368886113166809},{"id":"https://openalex.org/keywords/econometrics","display_name":"Econometrics","score":0.3239057660102844},{"id":"https://openalex.org/keywords/nonparametric-statistics","display_name":"Nonparametric statistics","score":0.1839616894721985}],"concepts":[{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.4589380621910095},{"id":"https://openalex.org/C78297888","wikidata":"https://www.wikidata.org/wiki/Q7449607","display_name":"Semiparametric model","level":3,"score":0.45814746618270874},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.4576714038848877},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.3546797037124634},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.3389757573604584},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3368886113166809},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.3239057660102844},{"id":"https://openalex.org/C102366305","wikidata":"https://www.wikidata.org/wiki/Q1097688","display_name":"Nonparametric statistics","level":2,"score":0.1839616894721985},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0},{"id":"https://openalex.org/C55493867","wikidata":"https://www.wikidata.org/wiki/Q7094","display_name":"Biochemistry","level":1,"score":0.0},{"id":"https://openalex.org/C104317684","wikidata":"https://www.wikidata.org/wiki/Q7187","display_name":"Gene","level":2,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tsp.2022.3230336","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tsp.2022.3230336","pdf_url":null,"source":{"id":"https://openalex.org/S168680287","display_name":"IEEE Transactions on Signal Processing","issn_l":"1053-587X","issn":["1053-587X","1941-0476"],"is_oa":false,"is_in_doaj":false,"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":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Signal Processing","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G413416892","display_name":null,"funder_award_id":"18H03211","funder_id":"https://openalex.org/F4320334764","funder_display_name":"Japan Society for the Promotion of Science"}],"funders":[{"id":"https://openalex.org/F4320334764","display_name":"Japan Society for the Promotion of Science","ror":"https://ror.org/00hhkn466"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":34,"referenced_works":["https://openalex.org/W221376126","https://openalex.org/W1563108151","https://openalex.org/W1939766099","https://openalex.org/W1965928820","https://openalex.org/W1968105193","https://openalex.org/W1979573625","https://openalex.org/W1982733046","https://openalex.org/W1988111769","https://openalex.org/W1989638282","https://openalex.org/W2008418230","https://openalex.org/W2010951077","https://openalex.org/W2014509644","https://openalex.org/W2017986148","https://openalex.org/W2034646922","https://openalex.org/W2049454545","https://openalex.org/W2054834816","https://openalex.org/W2063717353","https://openalex.org/W2089893077","https://openalex.org/W2098235563","https://openalex.org/W2142582388","https://openalex.org/W2145962650","https://openalex.org/W2146225536","https://openalex.org/W2478999501","https://openalex.org/W2555851076","https://openalex.org/W2673764533","https://openalex.org/W2963178286","https://openalex.org/W2963616494","https://openalex.org/W2973638981","https://openalex.org/W3004435768","https://openalex.org/W3100788021","https://openalex.org/W3111439133","https://openalex.org/W3116465768","https://openalex.org/W3190801418","https://openalex.org/W4249947721"],"related_works":["https://openalex.org/W2770593030","https://openalex.org/W3154990682","https://openalex.org/W4281727072","https://openalex.org/W2560201613","https://openalex.org/W2171975302","https://openalex.org/W1967666196","https://openalex.org/W112660553","https://openalex.org/W1486914010","https://openalex.org/W2170349014","https://openalex.org/W1981934814"],"abstract_inverted_index":{"Principal":[0],"component":[1],"analysis":[2,141],"(PCA)":[3],"is":[4,16,40,58,85,102,122],"one":[5],"of":[6,24,95,133,145],"the":[7,22,37,44,50,55,75,143],"most":[8,45],"popular":[9],"dimension":[10],"reduction":[11],"methods.":[12],"The":[13,93],"usual":[14],"PCA":[15,30],"known":[17],"to":[18,21,42,78,87,104,130],"be":[19,43,88],"sensitive":[20],"presence":[23],"outliers,":[25],"and":[26,60,109,127,138],"thus":[27],"many":[28],"robust":[29,46,81,103],"methods":[31],"have":[32],"been":[33],"developed.":[34],"Among":[35],"them,":[36],"Tyler's":[38,64,91],"M-estimator":[39,65],"shown":[41,86],"scatter":[47],"estimator":[48],"under":[49],"elliptical":[51,107],"distribution.":[52],"However,":[53],"when":[54],"underlying":[56],"distribution":[57],"contaminated":[59],"deviates":[61],"from":[62],"ellipticity,":[63],"might":[66],"not":[67],"work":[68],"well.":[69],"In":[70],"this":[71],"article,":[72],"we":[73],"apply":[74],"semiparametric":[76,82],"theory":[77],"propose":[79],"a":[80,89,117,139],"PCA,":[83],"which":[84,121],"re-weighted":[90],"M-estimator.":[92],"merits":[94],"our":[96,146],"proposal":[97],"are":[98],"twofold.":[99],"First,":[100],"it":[101,113],"both":[105],"heavy-tailed":[106],"outliers":[108],"non-elliptical":[110],"outliers.":[111],"Second,":[112],"pairs":[114],"well":[115],"with":[116],"data-driven":[118],"tuning":[119],"procedure,":[120],"based":[123],"on":[124],"active":[125],"ratio":[126],"can":[128],"adapt":[129],"different":[131],"degrees":[132],"data":[134,140],"outlyingness.":[135],"Simulation":[136],"studies":[137],"demonstrate":[142],"superiority":[144],"method.":[147]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":2}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2023-01-04T00:00:00"}