{"id":"https://openalex.org/W2102305760","doi":"https://doi.org/10.1109/icpr.2002.1047925","title":"Fractional component analysis (FCA) for mixed signals","display_name":"Fractional component analysis (FCA) for mixed signals","publication_year":2003,"publication_date":"2003-06-25","ids":{"openalex":"https://openalex.org/W2102305760","doi":"https://doi.org/10.1109/icpr.2002.1047925","mag":"2102305760"},"language":"en","primary_location":{"id":"doi:10.1109/icpr.2002.1047925","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icpr.2002.1047925","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Object recognition supported by user interaction for service robots","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/A5034077980","display_name":"Asanobu Kitamoto","orcid":"https://orcid.org/0000-0002-1517-7795"},"institutions":[{"id":"https://openalex.org/I184597095","display_name":"National Institute of Informatics","ror":"https://ror.org/04ksd4g47","country_code":"JP","type":"facility","lineage":["https://openalex.org/I1319490839","https://openalex.org/I184597095","https://openalex.org/I4210158934"]}],"countries":["JP"],"is_corresponding":true,"raw_author_name":"A. Kitamoto","raw_affiliation_strings":["National Institute of Informatics (NII), China","National Institute of Informatics,"],"affiliations":[{"raw_affiliation_string":"National Institute of Informatics (NII), China","institution_ids":[]},{"raw_affiliation_string":"National Institute of Informatics,","institution_ids":["https://openalex.org/I184597095"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5034077980"],"corresponding_institution_ids":["https://openalex.org/I184597095"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.18799472,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"3","issue":null,"first_page":"383","last_page":"386"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11447","display_name":"Blind Source Separation Techniques","score":0.9962000250816345,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"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/T11447","display_name":"Blind Source Separation Techniques","score":0.9962000250816345,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"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/T11901","display_name":"Bayesian Methods and Mixture Models","score":0.9948999881744385,"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/T10711","display_name":"Target Tracking and Data Fusion in Sensor Networks","score":0.9782999753952026,"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/randomness","display_name":"Randomness","score":0.6041154265403748},{"id":"https://openalex.org/keywords/component","display_name":"Component (thermodynamics)","score":0.570915937423706},{"id":"https://openalex.org/keywords/probability-density-function","display_name":"Probability density function","score":0.5506623387336731},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.48427385091781616},{"id":"https://openalex.org/keywords/expectation\u2013maximization-algorithm","display_name":"Expectation\u2013maximization algorithm","score":0.47885963320732117},{"id":"https://openalex.org/keywords/constraint","display_name":"Constraint (computer-aided design)","score":0.47767218947410583},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.4721408188343048},{"id":"https://openalex.org/keywords/maximization","display_name":"Maximization","score":0.4567400813102722},{"id":"https://openalex.org/keywords/component-analysis","display_name":"Component analysis","score":0.4562557339668274},{"id":"https://openalex.org/keywords/uniqueness","display_name":"Uniqueness","score":0.4510457217693329},{"id":"https://openalex.org/keywords/signal","display_name":"SIGNAL (programming language)","score":0.4360157251358032},{"id":"https://openalex.org/keywords/independent-component-analysis","display_name":"Independent component analysis","score":0.4248404800891876},{"id":"https://openalex.org/keywords/mathematical-optimization","display_name":"Mathematical optimization","score":0.40638798475265503},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.39709219336509705},{"id":"https://openalex.org/keywords/applied-mathematics","display_name":"Applied mathematics","score":0.34577223658561707},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3335801959037781},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.16576844453811646},{"id":"https://openalex.org/keywords/maximum-likelihood","display_name":"Maximum likelihood","score":0.16079577803611755}],"concepts":[{"id":"https://openalex.org/C125112378","wikidata":"https://www.wikidata.org/wiki/Q176640","display_name":"Randomness","level":2,"score":0.6041154265403748},{"id":"https://openalex.org/C168167062","wikidata":"https://www.wikidata.org/wiki/Q1117970","display_name":"Component (thermodynamics)","level":2,"score":0.570915937423706},{"id":"https://openalex.org/C197055811","wikidata":"https://www.wikidata.org/wiki/Q207522","display_name":"Probability density function","level":2,"score":0.5506623387336731},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.48427385091781616},{"id":"https://openalex.org/C182081679","wikidata":"https://www.wikidata.org/wiki/Q1275153","display_name":"Expectation\u2013maximization algorithm","level":3,"score":0.47885963320732117},{"id":"https://openalex.org/C2776036281","wikidata":"https://www.wikidata.org/wiki/Q48769818","display_name":"Constraint (computer-aided design)","level":2,"score":0.47767218947410583},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.4721408188343048},{"id":"https://openalex.org/C2776330181","wikidata":"https://www.wikidata.org/wiki/Q18358244","display_name":"Maximization","level":2,"score":0.4567400813102722},{"id":"https://openalex.org/C2780692498","wikidata":"https://www.wikidata.org/wiki/Q16950721","display_name":"Component analysis","level":2,"score":0.4562557339668274},{"id":"https://openalex.org/C2777021972","wikidata":"https://www.wikidata.org/wiki/Q22976830","display_name":"Uniqueness","level":2,"score":0.4510457217693329},{"id":"https://openalex.org/C2779843651","wikidata":"https://www.wikidata.org/wiki/Q7390335","display_name":"SIGNAL (programming language)","level":2,"score":0.4360157251358032},{"id":"https://openalex.org/C51432778","wikidata":"https://www.wikidata.org/wiki/Q1259145","display_name":"Independent component analysis","level":2,"score":0.4248404800891876},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.40638798475265503},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.39709219336509705},{"id":"https://openalex.org/C28826006","wikidata":"https://www.wikidata.org/wiki/Q33521","display_name":"Applied mathematics","level":1,"score":0.34577223658561707},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3335801959037781},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.16576844453811646},{"id":"https://openalex.org/C49781872","wikidata":"https://www.wikidata.org/wiki/Q1045555","display_name":"Maximum likelihood","level":2,"score":0.16079577803611755},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","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/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0},{"id":"https://openalex.org/C97355855","wikidata":"https://www.wikidata.org/wiki/Q11473","display_name":"Thermodynamics","level":1,"score":0.0}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.1109/icpr.2002.1047925","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icpr.2002.1047925","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Object recognition supported by user interaction for service robots","raw_type":"proceedings-article"},{"id":"pmh:oai:CiteSeerX.psu:10.1.1.12.8399","is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.12.8399","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://agora.ex.nii.ac.jp/~kitamoto/research/publications/./k:icpr02.pdf","raw_type":"text"},{"id":"pmh:oai:CiteSeerX.psu:10.1.1.145.6902","is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.145.6902","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://agora.ex.nii.ac.jp/~kitamoto/research/publications/k:icpr02.pdf","raw_type":"text"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Peace, Justice and strong institutions","id":"https://metadata.un.org/sdg/16","score":0.7200000286102295}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":8,"referenced_works":["https://openalex.org/W1548802052","https://openalex.org/W1569386697","https://openalex.org/W2001617957","https://openalex.org/W2117853077","https://openalex.org/W2120248066","https://openalex.org/W4205778870","https://openalex.org/W6633983736","https://openalex.org/W6677762038"],"related_works":["https://openalex.org/W1971575144","https://openalex.org/W2369494890","https://openalex.org/W2139404519","https://openalex.org/W1513845058","https://openalex.org/W2121025724","https://openalex.org/W2393502243","https://openalex.org/W2366752067","https://openalex.org/W2123927273","https://openalex.org/W3112853371","https://openalex.org/W2370924545"],"abstract_inverted_index":{"This":[0],"paper":[1,51,105],"proposes":[2],"fractional":[3],"component":[4,16],"analysis":[5],"(FCA),":[6],"whose":[7],"goal":[8],"is":[9,33,91],"to":[10,76,92,112],"decompose":[11],"the":[12,25,34,37,48,53,70,88,94,97,104,114,120],"observed":[13],"signal":[14,45],"into":[15,69],"signals":[17],"and":[18,62,65],"recover":[19],"their":[20],"fractions.":[21],"The":[22,50],"uniqueness":[23],"of":[24,36,79,96],"idea":[26],"in":[27],"comparison":[28],"with":[29],"other":[30],"similar":[31],"methods":[32],"concept":[35],"virtual":[38,54,98],"PDF":[39,55,99],"(probability":[40],"distribution":[41],"function)":[42],"that":[43],"models":[44],"mixing":[46],"on":[47,57,109],"sensor.":[49],"derives":[52],"based":[56],"positivity":[58],"constraint,":[59,61],"unity":[60],"randomness":[63],"assumption,":[64],"then":[66],"builds":[67],"it":[68],"mixture":[71],"density":[72],"model.":[73],"In":[74],"order":[75],"learn":[77],"parameters":[78],"this":[80],"model":[81],"from":[82,119],"data":[83,111],"using":[84,100],"EM":[85],"(Expectation-Maximization)":[86],"algorithm,":[87],"key":[89],"point":[90],"derive":[93],"approximation":[95],"its":[101],"cumulants.":[102],"Finally":[103],"illustrates":[106],"experimental":[107],"results":[108],"synthetic":[110],"show":[113],"unique":[115],"decision":[116],"boundary":[117],"obtained":[118],"method.":[121]},"counts_by_year":[],"updated_date":"2026-04-05T17:49:38.594831","created_date":"2025-10-10T00:00:00"}
