{"id":"https://openalex.org/W2084717150","doi":"https://doi.org/10.1109/cip.2010.5604118","title":"Bayesian and pairwise local similarity discriminant analysis","display_name":"Bayesian and pairwise local similarity discriminant analysis","publication_year":2010,"publication_date":"2010-06-01","ids":{"openalex":"https://openalex.org/W2084717150","doi":"https://doi.org/10.1109/cip.2010.5604118","mag":"2084717150"},"language":"en","primary_location":{"id":"doi:10.1109/cip.2010.5604118","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cip.2010.5604118","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2010 2nd International Workshop on Cognitive Information Processing","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/A5083550114","display_name":"Peter Sadowski","orcid":"https://orcid.org/0000-0002-7354-5461"},"institutions":[{"id":"https://openalex.org/I58610484","display_name":"Seattle University","ror":"https://ror.org/02jqc0m91","country_code":"US","type":"education","lineage":["https://openalex.org/I58610484"]},{"id":"https://openalex.org/I201448701","display_name":"University of Washington","ror":"https://ror.org/00cvxb145","country_code":"US","type":"education","lineage":["https://openalex.org/I201448701"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Peter Sadowski","raw_affiliation_strings":["Department Electrical Engineering, University of Washington, Seattle, USA","Dept. Electrical Engineering, University of Washington, Seattle, USA"],"affiliations":[{"raw_affiliation_string":"Department Electrical Engineering, University of Washington, Seattle, USA","institution_ids":["https://openalex.org/I201448701","https://openalex.org/I58610484"]},{"raw_affiliation_string":"Dept. Electrical Engineering, University of Washington, Seattle, USA","institution_ids":["https://openalex.org/I201448701","https://openalex.org/I58610484"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5026977722","display_name":"Luca Cazzanti","orcid":null},"institutions":[{"id":"https://openalex.org/I201448701","display_name":"University of Washington","ror":"https://ror.org/00cvxb145","country_code":"US","type":"education","lineage":["https://openalex.org/I201448701"]},{"id":"https://openalex.org/I4210138199","display_name":"University of Washington Applied Physics Laboratory","ror":"https://ror.org/03d17d270","country_code":"US","type":"facility","lineage":["https://openalex.org/I201448701","https://openalex.org/I4210138199"]},{"id":"https://openalex.org/I58610484","display_name":"Seattle University","ror":"https://ror.org/02jqc0m91","country_code":"US","type":"education","lineage":["https://openalex.org/I58610484"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Luca Cazzanti","raw_affiliation_strings":["Applied Physics Laboratory, University of Washington, Seattle, USA","Applied Physics Laboratory , University of Washington , Seattle , USA"],"affiliations":[{"raw_affiliation_string":"Applied Physics Laboratory, University of Washington, Seattle, USA","institution_ids":["https://openalex.org/I4210138199","https://openalex.org/I58610484"]},{"raw_affiliation_string":"Applied Physics Laboratory , University of Washington , Seattle , USA","institution_ids":["https://openalex.org/I201448701"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5111921762","display_name":"Maya R. Gupta","orcid":null},"institutions":[{"id":"https://openalex.org/I201448701","display_name":"University of Washington","ror":"https://ror.org/00cvxb145","country_code":"US","type":"education","lineage":["https://openalex.org/I201448701"]},{"id":"https://openalex.org/I58610484","display_name":"Seattle University","ror":"https://ror.org/02jqc0m91","country_code":"US","type":"education","lineage":["https://openalex.org/I58610484"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Maya R. Gupta","raw_affiliation_strings":["Department Electrical Engineering, University of Washington, Seattle, USA","Dept. Electrical Engineering, University of Washington, Seattle, USA"],"affiliations":[{"raw_affiliation_string":"Department Electrical Engineering, University of Washington, Seattle, USA","institution_ids":["https://openalex.org/I201448701","https://openalex.org/I58610484"]},{"raw_affiliation_string":"Dept. Electrical Engineering, University of Washington, Seattle, USA","institution_ids":["https://openalex.org/I201448701","https://openalex.org/I58610484"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5083550114"],"corresponding_institution_ids":["https://openalex.org/I201448701","https://openalex.org/I58610484"],"apc_list":null,"apc_paid":null,"fwci":0.6374,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.71324005,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"11","issue":null,"first_page":"287","last_page":"292"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10057","display_name":"Face and Expression Recognition","score":0.9911999702453613,"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":0.9911999702453613,"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/T11901","display_name":"Bayesian Methods and Mixture Models","score":0.9869999885559082,"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/T11550","display_name":"Text and Document Classification Technologies","score":0.9776999950408936,"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/pairwise-comparison","display_name":"Pairwise comparison","score":0.7945709228515625},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6879302263259888},{"id":"https://openalex.org/keywords/similarity","display_name":"Similarity (geometry)","score":0.6140792369842529},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.584892988204956},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5799299478530884},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.5755733251571655},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.5593001246452332},{"id":"https://openalex.org/keywords/linear-discriminant-analysis","display_name":"Linear discriminant analysis","score":0.4912235140800476},{"id":"https://openalex.org/keywords/generative-grammar","display_name":"Generative grammar","score":0.4715785086154938},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.45721837878227234},{"id":"https://openalex.org/keywords/dirichlet-distribution","display_name":"Dirichlet distribution","score":0.4524461627006531},{"id":"https://openalex.org/keywords/regularization","display_name":"Regularization (linguistics)","score":0.4505425691604614},{"id":"https://openalex.org/keywords/latent-dirichlet-allocation","display_name":"Latent Dirichlet allocation","score":0.44304805994033813},{"id":"https://openalex.org/keywords/generative-model","display_name":"Generative model","score":0.433938592672348},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.35129761695861816},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.34975045919418335},{"id":"https://openalex.org/keywords/topic-model","display_name":"Topic model","score":0.1618105173110962}],"concepts":[{"id":"https://openalex.org/C184898388","wikidata":"https://www.wikidata.org/wiki/Q1435712","display_name":"Pairwise comparison","level":2,"score":0.7945709228515625},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6879302263259888},{"id":"https://openalex.org/C103278499","wikidata":"https://www.wikidata.org/wiki/Q254465","display_name":"Similarity (geometry)","level":3,"score":0.6140792369842529},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.584892988204956},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5799299478530884},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.5755733251571655},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.5593001246452332},{"id":"https://openalex.org/C69738355","wikidata":"https://www.wikidata.org/wiki/Q1228929","display_name":"Linear discriminant analysis","level":2,"score":0.4912235140800476},{"id":"https://openalex.org/C39890363","wikidata":"https://www.wikidata.org/wiki/Q36108","display_name":"Generative grammar","level":2,"score":0.4715785086154938},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.45721837878227234},{"id":"https://openalex.org/C169214877","wikidata":"https://www.wikidata.org/wiki/Q981016","display_name":"Dirichlet distribution","level":3,"score":0.4524461627006531},{"id":"https://openalex.org/C2776135515","wikidata":"https://www.wikidata.org/wiki/Q17143721","display_name":"Regularization (linguistics)","level":2,"score":0.4505425691604614},{"id":"https://openalex.org/C500882744","wikidata":"https://www.wikidata.org/wiki/Q269236","display_name":"Latent Dirichlet allocation","level":3,"score":0.44304805994033813},{"id":"https://openalex.org/C167966045","wikidata":"https://www.wikidata.org/wiki/Q5532625","display_name":"Generative model","level":3,"score":0.433938592672348},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.35129761695861816},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.34975045919418335},{"id":"https://openalex.org/C171686336","wikidata":"https://www.wikidata.org/wiki/Q3532085","display_name":"Topic model","level":2,"score":0.1618105173110962},{"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/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/cip.2010.5604118","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cip.2010.5604118","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2010 2nd International Workshop on Cognitive Information Processing","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/10","display_name":"Reduced inequalities","score":0.7699999809265137}],"awards":[],"funders":[{"id":"https://openalex.org/F4320324887","display_name":"Shandong Academy of Sciences","ror":"https://ror.org/04y8d6y55"},{"id":"https://openalex.org/F4320337345","display_name":"Office of Naval Research","ror":"https://ror.org/00rk2pe57"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":21,"referenced_works":["https://openalex.org/W190008395","https://openalex.org/W2002394585","https://openalex.org/W2004131797","https://openalex.org/W2064485497","https://openalex.org/W2069429561","https://openalex.org/W2077990749","https://openalex.org/W2107955295","https://openalex.org/W2115933183","https://openalex.org/W2118216287","https://openalex.org/W2123167923","https://openalex.org/W2124735751","https://openalex.org/W2125369647","https://openalex.org/W2148894497","https://openalex.org/W2157656721","https://openalex.org/W2159009490","https://openalex.org/W2165828254","https://openalex.org/W2614053927","https://openalex.org/W3120740533","https://openalex.org/W6677301753","https://openalex.org/W6677557080","https://openalex.org/W6683291568"],"related_works":["https://openalex.org/W3126382579","https://openalex.org/W3107650560","https://openalex.org/W4315588616","https://openalex.org/W4317422773","https://openalex.org/W2888805565","https://openalex.org/W2810542905","https://openalex.org/W2129350855","https://openalex.org/W2891616219","https://openalex.org/W2497860580","https://openalex.org/W3204672119"],"abstract_inverted_index":{"We":[0,60],"investigate":[1],"three":[2],"extensions":[3,65,105],"to":[4,19,51],"the":[5,21,25,34,53,63,94,103,109,114],"generative":[6],"similarity-based":[7,89],"classifier":[8],"called":[9],"local":[10,42,48,72,81,115],"similarity":[11],"discriminant":[12],"analysis":[13],"(local":[14],"SDA):":[15],"a":[16,38,55],"Bayesian":[17,57],"approach":[18],"estimating":[20],"pmfs":[22],"based":[23],"on":[24,33],"assumption":[26],"that":[27,44,93],"similarities":[28,50],"are":[29,106],"multinomially":[30],"distributed":[31],"and":[32,74,98],"Dirichlet":[35],"prior":[36],"distribution;":[37],"pairwise-similarity":[39,58],"formulation":[40],"of":[41,102,113],"SDA":[43,73,82,116],"accounts":[45],"for":[46],"all":[47],"pairwise":[49],"estimate":[52],"pmfs;":[54],"combined":[56],"approach.":[59],"discuss":[61],"how":[62],"proposed":[64,104],"afford":[66],"more":[67],"modeling":[68,96],"flexibility":[69,97],"than":[70,79],"standard":[71],"less":[75],"cumbersome":[76],"model":[77],"training":[78],"previously-published":[80],"regularization":[83],"strategies.":[84],"Experiments":[85],"with":[86,108],"five":[87],"benchmark":[88],"classification":[90,111,117],"datasets":[91],"show":[92],"increased":[95],"lighter":[99],"computational":[100],"burden":[101],"coupled":[107],"good":[110],"performance":[112],"paradigm.":[118]},"counts_by_year":[],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
