{"id":"https://openalex.org/W1645191920","doi":"https://doi.org/10.3233/ida-150761","title":"LDA-based probabilistic graphical model for excitation-emission matrices","display_name":"LDA-based probabilistic graphical model for excitation-emission matrices","publication_year":2015,"publication_date":"2015-09-08","ids":{"openalex":"https://openalex.org/W1645191920","doi":"https://doi.org/10.3233/ida-150761","mag":"1645191920"},"language":"en","primary_location":{"id":"doi:10.3233/ida-150761","is_oa":false,"landing_page_url":"https://doi.org/10.3233/ida-150761","pdf_url":null,"source":{"id":"https://openalex.org/S2498839158","display_name":"Intelligent Data Analysis","issn_l":"1088-467X","issn":["1088-467X","1571-4128"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310318577","host_organization_name":"IOS Press","host_organization_lineage":["https://openalex.org/P4310318577"],"host_organization_lineage_names":["IOS Press"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Intelligent Data Analysis","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/A5075851385","display_name":"Oscar Martinez","orcid":null},"institutions":[{"id":"https://openalex.org/I145608581","display_name":"University of Miami","ror":"https://ror.org/02dgjyy92","country_code":"US","type":"education","lineage":["https://openalex.org/I145608581"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Oscar Martinez","raw_affiliation_strings":["University of Miami,"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Miami,","institution_ids":["https://openalex.org/I145608581"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5037980982","display_name":"Ranga Dabarera","orcid":null},"institutions":[{"id":"https://openalex.org/I145608581","display_name":"University of Miami","ror":"https://ror.org/02dgjyy92","country_code":"US","type":"education","lineage":["https://openalex.org/I145608581"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Ranga Dabarera","raw_affiliation_strings":["University of Miami,"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Miami,","institution_ids":["https://openalex.org/I145608581"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5018408973","display_name":"Kamal Premaratne","orcid":"https://orcid.org/0000-0001-5975-4403"},"institutions":[{"id":"https://openalex.org/I145608581","display_name":"University of Miami","ror":"https://ror.org/02dgjyy92","country_code":"US","type":"education","lineage":["https://openalex.org/I145608581"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Kamal Premaratne","raw_affiliation_strings":["University of Miami, Coral Gables, United States"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Miami, Coral Gables, United States","institution_ids":["https://openalex.org/I145608581"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5005267732","display_name":"Miroslav Kub\u00e1t","orcid":"https://orcid.org/0000-0002-3398-3125"},"institutions":[{"id":"https://openalex.org/I145608581","display_name":"University of Miami","ror":"https://ror.org/02dgjyy92","country_code":"US","type":"education","lineage":["https://openalex.org/I145608581"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Miroslav Kubat","raw_affiliation_strings":["University of Miami, Coral Gables, United States"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Miami, Coral Gables, United States","institution_ids":["https://openalex.org/I145608581"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5075851385"],"corresponding_institution_ids":["https://openalex.org/I145608581"],"apc_list":null,"apc_paid":null,"fwci":0.9719,"has_fulltext":false,"cited_by_count":4,"citation_normalized_percentile":{"value":0.72376701,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":94,"max":96},"biblio":{"volume":"19","issue":"5","first_page":"1109","last_page":"1130"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10640","display_name":"Spectroscopy and Chemometric Analyses","score":0.9977999925613403,"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"}},"topics":[{"id":"https://openalex.org/T10640","display_name":"Spectroscopy and Chemometric Analyses","score":0.9977999925613403,"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/T11447","display_name":"Blind Source Separation Techniques","score":0.9923999905586243,"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/T10885","display_name":"Gene expression and cancer classification","score":0.9753000140190125,"subfield":{"id":"https://openalex.org/subfields/1312","display_name":"Molecular Biology"},"field":{"id":"https://openalex.org/fields/13","display_name":"Biochemistry, Genetics and Molecular Biology"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/latent-dirichlet-allocation","display_name":"Latent Dirichlet allocation","score":0.8605122566223145},{"id":"https://openalex.org/keywords/probabilistic-logic","display_name":"Probabilistic logic","score":0.7340580821037292},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.6999344229698181},{"id":"https://openalex.org/keywords/graphical-model","display_name":"Graphical model","score":0.6443759202957153},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5761253833770752},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5665221214294434},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5239889025688171},{"id":"https://openalex.org/keywords/topic-model","display_name":"Topic model","score":0.5027141571044922},{"id":"https://openalex.org/keywords/dirichlet-distribution","display_name":"Dirichlet distribution","score":0.46233105659484863},{"id":"https://openalex.org/keywords/statistical-model","display_name":"Statistical model","score":0.4609359800815582},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.44079506397247314},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.43623244762420654},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.33001136779785156},{"id":"https://openalex.org/keywords/chemistry","display_name":"Chemistry","score":0.1348576843738556}],"concepts":[{"id":"https://openalex.org/C500882744","wikidata":"https://www.wikidata.org/wiki/Q269236","display_name":"Latent Dirichlet allocation","level":3,"score":0.8605122566223145},{"id":"https://openalex.org/C49937458","wikidata":"https://www.wikidata.org/wiki/Q2599292","display_name":"Probabilistic logic","level":2,"score":0.7340580821037292},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.6999344229698181},{"id":"https://openalex.org/C155846161","wikidata":"https://www.wikidata.org/wiki/Q1143367","display_name":"Graphical model","level":2,"score":0.6443759202957153},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5761253833770752},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5665221214294434},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5239889025688171},{"id":"https://openalex.org/C171686336","wikidata":"https://www.wikidata.org/wiki/Q3532085","display_name":"Topic model","level":2,"score":0.5027141571044922},{"id":"https://openalex.org/C169214877","wikidata":"https://www.wikidata.org/wiki/Q981016","display_name":"Dirichlet distribution","level":3,"score":0.46233105659484863},{"id":"https://openalex.org/C114289077","wikidata":"https://www.wikidata.org/wiki/Q3284399","display_name":"Statistical model","level":2,"score":0.4609359800815582},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.44079506397247314},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.43623244762420654},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.33001136779785156},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.1348576843738556},{"id":"https://openalex.org/C104317684","wikidata":"https://www.wikidata.org/wiki/Q7187","display_name":"Gene","level":2,"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/C17744445","wikidata":"https://www.wikidata.org/wiki/Q36442","display_name":"Political science","level":0,"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/C182310444","wikidata":"https://www.wikidata.org/wiki/Q1332643","display_name":"Boundary value problem","level":2,"score":0.0},{"id":"https://openalex.org/C199539241","wikidata":"https://www.wikidata.org/wiki/Q7748","display_name":"Law","level":1,"score":0.0},{"id":"https://openalex.org/C94625758","wikidata":"https://www.wikidata.org/wiki/Q7163","display_name":"Politics","level":2,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.3233/ida-150761","is_oa":false,"landing_page_url":"https://doi.org/10.3233/ida-150761","pdf_url":null,"source":{"id":"https://openalex.org/S2498839158","display_name":"Intelligent Data Analysis","issn_l":"1088-467X","issn":["1088-467X","1571-4128"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310318577","host_organization_name":"IOS Press","host_organization_lineage":["https://openalex.org/P4310318577"],"host_organization_lineage_names":["IOS Press"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Intelligent Data Analysis","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":32,"referenced_works":["https://openalex.org/W66588809","https://openalex.org/W101682403","https://openalex.org/W1880262756","https://openalex.org/W1939564553","https://openalex.org/W1965268372","https://openalex.org/W1968061328","https://openalex.org/W1969082792","https://openalex.org/W1994219736","https://openalex.org/W2000215628","https://openalex.org/W2001082470","https://openalex.org/W2013344533","https://openalex.org/W2017109326","https://openalex.org/W2019816938","https://openalex.org/W2042015642","https://openalex.org/W2045174037","https://openalex.org/W2053258992","https://openalex.org/W2057471518","https://openalex.org/W2061959974","https://openalex.org/W2066715310","https://openalex.org/W2069739265","https://openalex.org/W2077918660","https://openalex.org/W2084960421","https://openalex.org/W2091716858","https://openalex.org/W2116782162","https://openalex.org/W2119741678","https://openalex.org/W2121739212","https://openalex.org/W2129995310","https://openalex.org/W2140862024","https://openalex.org/W2165874743","https://openalex.org/W2408202704","https://openalex.org/W2490544534","https://openalex.org/W4285719527"],"related_works":["https://openalex.org/W2769501189","https://openalex.org/W4315588616","https://openalex.org/W4312773271","https://openalex.org/W2888805565","https://openalex.org/W2962686197","https://openalex.org/W3005513013","https://openalex.org/W2497860580","https://openalex.org/W2891616219","https://openalex.org/W2796920963","https://openalex.org/W3204672119"],"abstract_inverted_index":{"In":[0],"this":[1,54,115],"paper,":[2],"we":[3],"present":[4],"a":[5,44],"novel":[6],"latent":[7,45],"Dirichlet":[8],"allocation":[9],"(LDA)":[10],"based":[11],"probabilistic":[12],"graphical":[13],"approach":[14],"for":[15,82],"modeling":[16],"and":[17,78,107,127],"analyzing":[18],"fluorescent":[19],"spectroscopy":[20],"excitation-emission":[21],"Matrices":[22],"(EEMs).":[23],"By":[24],"viewing":[25],"the":[26,39,75,90],"EEMs":[27],"as":[28,73],"being":[29],"generated":[30],"from":[31],"an":[32,49,122],"underlying":[33],"hidden":[34],"pool":[35],"of":[36,48,105],"flourophore":[37],"compounds,":[38],"pro":[40],"posed":[41],"method":[42,117],"provides":[43],"flourophore-space":[46],"representation":[47],"EEM.":[50],"We":[51,110],"show":[52,88],"that":[53,89,113],"LDA-based":[55,92,116],"model":[56],"can":[57],"increase":[58],"classification":[59,125],"performance,":[60],"especially":[61],"when":[62],"paired":[63],"with":[64,84,118],"parallel":[65],"factor":[66],"analysis":[67],"(PARAFAC)":[68],"which":[69],"may":[70],"be":[71],"regarded":[72],"perhaps":[74],"most":[76],"popular":[77],"widely":[79],"used":[80],"tool":[81],"dealing":[83],"EEMs.":[85],"Our":[86],"experiments":[87],"proposed":[91],"algorithm":[93],"is":[94],"in":[95,124],"some":[96],"cases":[97],"more":[98],"robust":[99],"than":[100],"PARAFAC":[101,119],"to":[102,121,128],"certain":[103],"types":[104],"noise":[106],"data":[108],"disturbances.":[109],"also":[111],"observe":[112],"pairing":[114],"leads":[120],"improvement":[123],"performance":[126],"added":[129],"robustness":[130],"at":[131],"high":[132],"peak-signal-to-noise-ratio":[133],"(PSNR)":[134],"values.":[135]},"counts_by_year":[{"year":2017,"cited_by_count":2},{"year":2016,"cited_by_count":2}],"updated_date":"2026-06-23T06:36:01.041984","created_date":"2025-10-10T00:00:00"}