{"id":"https://openalex.org/W4317418525","doi":"https://doi.org/10.1109/gcce56475.2022.10014170","title":"A Consideration on Efficient Detection Method of Anormal Responses in High-dimensional Questionnaire Data","display_name":"A Consideration on Efficient Detection Method of Anormal Responses in High-dimensional Questionnaire Data","publication_year":2022,"publication_date":"2022-10-18","ids":{"openalex":"https://openalex.org/W4317418525","doi":"https://doi.org/10.1109/gcce56475.2022.10014170"},"language":"en","primary_location":{"id":"doi:10.1109/gcce56475.2022.10014170","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/gcce56475.2022.10014170","pdf_url":null,"source":{"id":"https://openalex.org/S4363607800","display_name":"2022 IEEE 11th Global Conference on Consumer Electronics (GCCE)","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":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 IEEE 11th Global Conference on Consumer Electronics (GCCE)","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/A5070562075","display_name":"Kosuke Kurosawa","orcid":"https://orcid.org/0000-0003-4965-4585"},"institutions":[{"id":"https://openalex.org/I150744194","display_name":"Waseda University","ror":"https://ror.org/00ntfnx83","country_code":"JP","type":"education","lineage":["https://openalex.org/I150744194"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Kosuke Kurosawa","raw_affiliation_strings":["Waseda University,Graduate School of Fundamental Science and Engineering,Tokyo,Japan","Graduate School of Fundamental Science and Engineering, Waseda University, Tokyo, Japan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Waseda University,Graduate School of Fundamental Science and Engineering,Tokyo,Japan","institution_ids":["https://openalex.org/I150744194"]},{"raw_affiliation_string":"Graduate School of Fundamental Science and Engineering, Waseda University, Tokyo, Japan","institution_ids":["https://openalex.org/I150744194"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5087166942","display_name":"Mutsumi Suganuma","orcid":null},"institutions":[{"id":"https://openalex.org/I201480613","display_name":"Tama University","ror":"https://ror.org/02gdq8g56","country_code":"JP","type":"education","lineage":["https://openalex.org/I201480613"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Mutsumi Suganuma","raw_affiliation_strings":["Tama University,School of Management and Information Sciences,Tokyo,Japan","School of Management and Information Sciences, Tama University, Tokyo, Japan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Tama University,School of Management and Information Sciences,Tokyo,Japan","institution_ids":["https://openalex.org/I201480613"]},{"raw_affiliation_string":"School of Management and Information Sciences, Tama University, Tokyo, Japan","institution_ids":["https://openalex.org/I201480613"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5084512417","display_name":"Wataru Kameyama","orcid":null},"institutions":[{"id":"https://openalex.org/I150744194","display_name":"Waseda University","ror":"https://ror.org/00ntfnx83","country_code":"JP","type":"education","lineage":["https://openalex.org/I150744194"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Wataru Kameyama","raw_affiliation_strings":["Waseda University,Faculty of Science and Engineering,Tokyo,Japan","Faculty of Science and Engineering, Waseda University, Tokyo, Japan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Waseda University,Faculty of Science and Engineering,Tokyo,Japan","institution_ids":["https://openalex.org/I150744194"]},{"raw_affiliation_string":"Faculty of Science and Engineering, Waseda University, Tokyo, Japan","institution_ids":["https://openalex.org/I150744194"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.18804572,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"12","issue":null,"first_page":"917","last_page":"918"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10057","display_name":"Face and Expression Recognition","score":0.9948999881744385,"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/T10057","display_name":"Face and Expression Recognition","score":0.9948999881744385,"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/T10500","display_name":"Sparse and Compressive Sensing Techniques","score":0.9828000068664551,"subfield":{"id":"https://openalex.org/subfields/2206","display_name":"Computational Mechanics"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10064","display_name":"Complex Network Analysis Techniques","score":0.9524999856948853,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/principal-component-analysis","display_name":"Principal component analysis","score":0.8594838380813599},{"id":"https://openalex.org/keywords/cluster-analysis","display_name":"Cluster analysis","score":0.6828464269638062},{"id":"https://openalex.org/keywords/dimension","display_name":"Dimension (graph theory)","score":0.646874189376831},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5757946372032166},{"id":"https://openalex.org/keywords/dimensionality-reduction","display_name":"Dimensionality reduction","score":0.5703198909759521},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5673820972442627},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5142121315002441},{"id":"https://openalex.org/keywords/gaussian","display_name":"Gaussian","score":0.5108940005302429},{"id":"https://openalex.org/keywords/multiple-correspondence-analysis","display_name":"Multiple correspondence analysis","score":0.4833347499370575},{"id":"https://openalex.org/keywords/matrix","display_name":"Matrix (chemical analysis)","score":0.4635187089443207},{"id":"https://openalex.org/keywords/cluster","display_name":"Cluster (spacecraft)","score":0.4550115466117859},{"id":"https://openalex.org/keywords/mixture-model","display_name":"Mixture model","score":0.4363536834716797},{"id":"https://openalex.org/keywords/high-dimensional","display_name":"High dimensional","score":0.42982712388038635},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.41730350255966187},{"id":"https://openalex.org/keywords/clustering-high-dimensional-data","display_name":"Clustering high-dimensional data","score":0.41711312532424927},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.29344284534454346},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.21844017505645752}],"concepts":[{"id":"https://openalex.org/C27438332","wikidata":"https://www.wikidata.org/wiki/Q2873","display_name":"Principal component analysis","level":2,"score":0.8594838380813599},{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.6828464269638062},{"id":"https://openalex.org/C33676613","wikidata":"https://www.wikidata.org/wiki/Q13415176","display_name":"Dimension (graph theory)","level":2,"score":0.646874189376831},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5757946372032166},{"id":"https://openalex.org/C70518039","wikidata":"https://www.wikidata.org/wiki/Q16000077","display_name":"Dimensionality reduction","level":2,"score":0.5703198909759521},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5673820972442627},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5142121315002441},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.5108940005302429},{"id":"https://openalex.org/C97448799","wikidata":"https://www.wikidata.org/wiki/Q2845212","display_name":"Multiple correspondence analysis","level":2,"score":0.4833347499370575},{"id":"https://openalex.org/C106487976","wikidata":"https://www.wikidata.org/wiki/Q685816","display_name":"Matrix (chemical analysis)","level":2,"score":0.4635187089443207},{"id":"https://openalex.org/C164866538","wikidata":"https://www.wikidata.org/wiki/Q367351","display_name":"Cluster (spacecraft)","level":2,"score":0.4550115466117859},{"id":"https://openalex.org/C61224824","wikidata":"https://www.wikidata.org/wiki/Q2260434","display_name":"Mixture model","level":2,"score":0.4363536834716797},{"id":"https://openalex.org/C3019722297","wikidata":"https://www.wikidata.org/wiki/Q4440864","display_name":"High dimensional","level":2,"score":0.42982712388038635},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.41730350255966187},{"id":"https://openalex.org/C184509293","wikidata":"https://www.wikidata.org/wiki/Q5136711","display_name":"Clustering high-dimensional data","level":3,"score":0.41711312532424927},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.29344284534454346},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.21844017505645752},{"id":"https://openalex.org/C192562407","wikidata":"https://www.wikidata.org/wiki/Q228736","display_name":"Materials science","level":0,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C159985019","wikidata":"https://www.wikidata.org/wiki/Q181790","display_name":"Composite material","level":1,"score":0.0},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0},{"id":"https://openalex.org/C202444582","wikidata":"https://www.wikidata.org/wiki/Q837863","display_name":"Pure mathematics","level":1,"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":1,"locations":[{"id":"doi:10.1109/gcce56475.2022.10014170","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/gcce56475.2022.10014170","pdf_url":null,"source":{"id":"https://openalex.org/S4363607800","display_name":"2022 IEEE 11th Global Conference on Consumer Electronics (GCCE)","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":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 IEEE 11th Global Conference on Consumer Electronics (GCCE)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":4,"referenced_works":["https://openalex.org/W2101234009","https://openalex.org/W2145962650","https://openalex.org/W4301419755","https://openalex.org/W6675354045"],"related_works":["https://openalex.org/W3162910294","https://openalex.org/W2196560602","https://openalex.org/W1974303229","https://openalex.org/W4324092386","https://openalex.org/W2962997812","https://openalex.org/W1692134900","https://openalex.org/W2126442420","https://openalex.org/W3196630240","https://openalex.org/W783379390","https://openalex.org/W2954934698"],"abstract_inverted_index":{"We":[0],"have":[1],"been":[2],"studying":[3],"to":[4,21,57,110,126],"detect":[5,112,127],"anormal":[6,72,108,113,129],"responses":[7,73,130],"in":[8,24,101],"high-dimensional":[9,59],"questionnaire":[10,60],"data,":[11],"that":[12,74],"may":[13],"affect":[14],"the":[15,25,58,65,79,83,94,102],"overall":[16],"analysis":[17,36,41],"results":[18],"and":[19,38,47,49,123],"are":[20,75],"be":[22],"removed":[23],"preprocess,":[26],"more":[27],"efficiently.":[28],"In":[29],"this":[30],"paper,":[31],"we":[32,63,87,118],"apply":[33],"principal":[34,89],"component":[35,90],"(PCA)":[37],"multiple":[39],"correspondence":[40],"(MCA)":[42],"as":[43,54,107],"dimension":[44],"reduction":[45],"methods,":[46],"x-means":[48],"gaussian":[50],"mixture":[51],"model":[52],"(GMM)":[53],"clustering":[55],"algorithms":[56],"data.":[61],"Then,":[62],"examine":[64],"combinations":[66],"of":[67,121],"these":[68],"methods":[69],"for":[70,98],"detecting":[71],"significantly":[76],"far":[77],"from":[78],"cluster":[80],"centers":[81],"or":[82],"distribution":[84],"centers.":[85],"Also,":[86],"employ":[88],"pursuit":[91],"(PCP),":[92],"where":[93],"absolute":[95],"value":[96],"sum":[97],"each":[99],"response":[100],"sparse":[103],"matrix":[104],"is":[105],"used":[106],"score":[109],"directly":[111],"responses.":[114],"As":[115],"a":[116],"result,":[117],"find":[119],"both":[120],"MCA+x-means":[122],"PCP":[124],"achieve":[125],"reasonable":[128],"with":[131],"shorter":[132],"execution":[133],"time.":[134]},"counts_by_year":[],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}
