{"id":"https://openalex.org/W2097579995","doi":"https://doi.org/10.1109/ijcnn.2008.4634142","title":"A pooled subspace mixture density model for pattern classification in high-dimensional spaces","display_name":"A pooled subspace mixture density model for pattern classification in high-dimensional spaces","publication_year":2008,"publication_date":"2008-06-01","ids":{"openalex":"https://openalex.org/W2097579995","doi":"https://doi.org/10.1109/ijcnn.2008.4634142","mag":"2097579995"},"language":"en","primary_location":{"id":"doi:10.1109/ijcnn.2008.4634142","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn.2008.4634142","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2008 IEEE International Joint Conference on Neural Networks (IEEE World Congress on Computational Intelligence)","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/A5100370790","display_name":"Xiaohua Liu","orcid":"https://orcid.org/0000-0003-0384-5431"},"institutions":[{"id":"https://openalex.org/I4210112150","display_name":"Institute of Automation","ror":"https://ror.org/022c3hy66","country_code":"CN","type":"facility","lineage":["https://openalex.org/I19820366","https://openalex.org/I4210112150"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Xiao-Hua Liu","raw_affiliation_strings":["National Laboratory of Pattern Recognition (NLPR) and the Sino-French Laboratory for Computer Science (LIAMA), Institute of Automation, Chinese Academy and Sciences, Beijing, China","Nat. Lab. of Pattern Recognition, Chinese Acad. of Sci., Beijing#TAB#"],"affiliations":[{"raw_affiliation_string":"National Laboratory of Pattern Recognition (NLPR) and the Sino-French Laboratory for Computer Science (LIAMA), Institute of Automation, Chinese Academy and Sciences, Beijing, China","institution_ids":["https://openalex.org/I4210112150"]},{"raw_affiliation_string":"Nat. Lab. of Pattern Recognition, Chinese Acad. of Sci., Beijing#TAB#","institution_ids":["https://openalex.org/I4210112150"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100714202","display_name":"Cheng\u2010Lin Liu","orcid":"https://orcid.org/0000-0002-6743-4175"},"institutions":[{"id":"https://openalex.org/I4210112150","display_name":"Institute of Automation","ror":"https://ror.org/022c3hy66","country_code":"CN","type":"facility","lineage":["https://openalex.org/I19820366","https://openalex.org/I4210112150"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Cheng-Lin Liu","raw_affiliation_strings":["National Laboratory of Pattern Recognition (NLPR) and the Sino-French Laboratory for Computer Science (LIAMA), Institute of Automation, Chinese Academy and Sciences, Beijing, China","Nat. Lab. of Pattern Recognition, Chinese Acad. of Sci., Beijing#TAB#"],"affiliations":[{"raw_affiliation_string":"National Laboratory of Pattern Recognition (NLPR) and the Sino-French Laboratory for Computer Science (LIAMA), Institute of Automation, Chinese Academy and Sciences, Beijing, China","institution_ids":["https://openalex.org/I4210112150"]},{"raw_affiliation_string":"Nat. Lab. of Pattern Recognition, Chinese Acad. of Sci., Beijing#TAB#","institution_ids":["https://openalex.org/I4210112150"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5010013507","display_name":"Xinwen Hou","orcid":"https://orcid.org/0000-0002-8468-001X"},"institutions":[{"id":"https://openalex.org/I4210112150","display_name":"Institute of Automation","ror":"https://ror.org/022c3hy66","country_code":"CN","type":"facility","lineage":["https://openalex.org/I19820366","https://openalex.org/I4210112150"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xinwen Hou","raw_affiliation_strings":["National Laboratory of Pattern Recognition (NLPR) and the Sino-French Laboratory for Computer Science (LIAMA), Institute of Automation, Chinese Academy and Sciences, Beijing, China","Nat. Lab. of Pattern Recognition, Chinese Acad. of Sci., Beijing#TAB#"],"affiliations":[{"raw_affiliation_string":"National Laboratory of Pattern Recognition (NLPR) and the Sino-French Laboratory for Computer Science (LIAMA), Institute of Automation, Chinese Academy and Sciences, Beijing, China","institution_ids":["https://openalex.org/I4210112150"]},{"raw_affiliation_string":"Nat. Lab. of Pattern Recognition, Chinese Acad. of Sci., Beijing#TAB#","institution_ids":["https://openalex.org/I4210112150"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5100370790"],"corresponding_institution_ids":["https://openalex.org/I4210112150"],"apc_list":null,"apc_paid":null,"fwci":1.1441,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.8419152,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"2466","last_page":"2471"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11901","display_name":"Bayesian Methods and Mixture Models","score":0.9987000226974487,"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":0.9987000226974487,"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/T10824","display_name":"Image Retrieval and Classification Techniques","score":0.9927999973297119,"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/T10057","display_name":"Face and Expression Recognition","score":0.9912999868392944,"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/subspace-topology","display_name":"Subspace topology","score":0.9306740760803223},{"id":"https://openalex.org/keywords/linear-subspace","display_name":"Linear subspace","score":0.8320997357368469},{"id":"https://openalex.org/keywords/density-estimation","display_name":"Density estimation","score":0.636266827583313},{"id":"https://openalex.org/keywords/mixture-model","display_name":"Mixture model","score":0.625137984752655},{"id":"https://openalex.org/keywords/gaussian","display_name":"Gaussian","score":0.6159504652023315},{"id":"https://openalex.org/keywords/curse-of-dimensionality","display_name":"Curse of dimensionality","score":0.594680666923523},{"id":"https://openalex.org/keywords/random-subspace-method","display_name":"Random subspace method","score":0.5933570861816406},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5625961422920227},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5008277893066406},{"id":"https://openalex.org/keywords/probability-density-function","display_name":"Probability density function","score":0.48790451884269714},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.46393755078315735},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.45060914754867554},{"id":"https://openalex.org/keywords/class","display_name":"Class (philosophy)","score":0.44356369972229004},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.3543521463871002},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.22473496198654175},{"id":"https://openalex.org/keywords/pure-mathematics","display_name":"Pure mathematics","score":0.06738191843032837},{"id":"https://openalex.org/keywords/physics","display_name":"Physics","score":0.06728875637054443}],"concepts":[{"id":"https://openalex.org/C32834561","wikidata":"https://www.wikidata.org/wiki/Q660730","display_name":"Subspace topology","level":2,"score":0.9306740760803223},{"id":"https://openalex.org/C12362212","wikidata":"https://www.wikidata.org/wiki/Q728435","display_name":"Linear subspace","level":2,"score":0.8320997357368469},{"id":"https://openalex.org/C189508267","wikidata":"https://www.wikidata.org/wiki/Q17088227","display_name":"Density estimation","level":3,"score":0.636266827583313},{"id":"https://openalex.org/C61224824","wikidata":"https://www.wikidata.org/wiki/Q2260434","display_name":"Mixture model","level":2,"score":0.625137984752655},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.6159504652023315},{"id":"https://openalex.org/C111030470","wikidata":"https://www.wikidata.org/wiki/Q1430460","display_name":"Curse of dimensionality","level":2,"score":0.594680666923523},{"id":"https://openalex.org/C106135958","wikidata":"https://www.wikidata.org/wiki/Q7291993","display_name":"Random subspace method","level":3,"score":0.5933570861816406},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5625961422920227},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5008277893066406},{"id":"https://openalex.org/C197055811","wikidata":"https://www.wikidata.org/wiki/Q207522","display_name":"Probability density function","level":2,"score":0.48790451884269714},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.46393755078315735},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.45060914754867554},{"id":"https://openalex.org/C2777212361","wikidata":"https://www.wikidata.org/wiki/Q5127848","display_name":"Class (philosophy)","level":2,"score":0.44356369972229004},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3543521463871002},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.22473496198654175},{"id":"https://openalex.org/C202444582","wikidata":"https://www.wikidata.org/wiki/Q837863","display_name":"Pure mathematics","level":1,"score":0.06738191843032837},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.06728875637054443},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","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}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ijcnn.2008.4634142","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn.2008.4634142","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2008 IEEE International Joint Conference on Neural Networks (IEEE World Congress on Computational Intelligence)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":17,"referenced_works":["https://openalex.org/W1551188508","https://openalex.org/W1985690171","https://openalex.org/W1999950466","https://openalex.org/W2003461835","https://openalex.org/W2015245929","https://openalex.org/W2049633694","https://openalex.org/W2084812512","https://openalex.org/W2094818450","https://openalex.org/W2111842831","https://openalex.org/W2115092992","https://openalex.org/W2125027820","https://openalex.org/W2128716185","https://openalex.org/W2132549764","https://openalex.org/W2142635246","https://openalex.org/W2146610201","https://openalex.org/W2168175751","https://openalex.org/W6677345854"],"related_works":["https://openalex.org/W2094490861","https://openalex.org/W2017090935","https://openalex.org/W63219142","https://openalex.org/W1570464650","https://openalex.org/W1528820368","https://openalex.org/W2919740138","https://openalex.org/W2187148935","https://openalex.org/W1569550976","https://openalex.org/W2543710962","https://openalex.org/W2043285515"],"abstract_inverted_index":{"Density":[0],"estimation":[1],"in":[2,36,72,85,97],"high-dimensional":[3],"data":[4,14],"spaces":[5],"is":[6,16,70],"a":[7,45,64],"challenge":[8],"due":[9],"to":[10,26,94],"the":[11,34,37,57,67,73,91,104,114,117],"sparseness":[12],"of":[13,21,60,116],"which":[15],"known":[17],"as":[18],"ldquothe":[19],"curse":[20],"dimensionalityrdquo.":[22],"Researchers":[23],"often":[24],"resort":[25],"low-dimensional":[27],"subspaces":[28],"for":[29],"such":[30],"tasks,":[31],"while":[32],"discard":[33],"distribution":[35],"complementary":[38,68],"subspace.":[39,53],"In":[40,54],"this":[41],"paper,":[42],"we":[43],"propose":[44],"new":[46],"mixture":[47,80],"density":[48,74,81,92],"model":[49,93],"based":[50],"on":[51,99],"pooled":[52],"our":[55],"method,":[56],"Gaussian":[58,79],"components":[59],"each":[61],"class":[62],"share":[63],"subspace":[65,69,77],"and":[66,78,102],"incorporated":[71],"function.":[75],"The":[76,110],"are":[82],"estimated":[83],"simultaneously":[84],"EM":[86],"iteration":[87],"steps.":[88],"We":[89],"apply":[90],"pattern":[95],"classification":[96],"experiments":[98],"UCI":[100],"datasets":[101],"compare":[103],"proposed":[105,118],"method":[106],"with":[107],"previous":[108],"ones.":[109],"experimental":[111],"results":[112],"demonstrate":[113],"superiority":[115],"method.":[119]},"counts_by_year":[],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
