@prefix dbo: .
@prefix dbr: .
dbr:Document-term_matrix dbo:wikiPageWikiLink dbr:Probabilistic_latent_semantic_analysis .
dbr:Collaborative_filtering dbo:wikiPageWikiLink dbr:Probabilistic_latent_semantic_analysis .
dbr:Object_categorization_from_image_search dbo:wikiPageWikiLink dbr:Probabilistic_latent_semantic_analysis .
@prefix foaf: .
@prefix wikipedia-en: .
wikipedia-en:Probabilistic_latent_semantic_analysis foaf:primaryTopic dbr:Probabilistic_latent_semantic_analysis .
dbr:Non-negative_matrix_factorization dbo:wikiPageWikiLink dbr:Probabilistic_latent_semantic_analysis .
dbr:Latent_semantic_analysis dbo:wikiPageWikiLink dbr:Probabilistic_latent_semantic_analysis .
dbr:List_of_statistics_articles dbo:wikiPageWikiLink dbr:Probabilistic_latent_semantic_analysis .
dbr:Fisher_kernel dbo:wikiPageWikiLink dbr:Probabilistic_latent_semantic_analysis .
dbr:Latent_Dirichlet_allocation dbo:wikiPageWikiLink dbr:Probabilistic_latent_semantic_analysis .
dbr:Probabilistic_latent_semantic_indexing dbo:wikiPageWikiLink dbr:Probabilistic_latent_semantic_analysis ;
dbo:wikiPageRedirects dbr:Probabilistic_latent_semantic_analysis .
dbr:Latent_and_observable_variables dbo:wikiPageWikiLink dbr:Probabilistic_latent_semantic_analysis .
dbr:Bag-of-words_model_in_computer_vision dbo:wikiPageWikiLink dbr:Probabilistic_latent_semantic_analysis .
dbr:Latent_class_model dbo:wikiPageWikiLink dbr:Probabilistic_latent_semantic_analysis .
dbr:Outline_of_machine_learning dbo:wikiPageWikiLink dbr:Probabilistic_latent_semantic_analysis .
dbr:PLSA dbo:wikiPageWikiLink dbr:Probabilistic_latent_semantic_analysis ;
dbo:wikiPageRedirects dbr:Probabilistic_latent_semantic_analysis .
dbr:Recommind dbo:wikiPageWikiLink dbr:Probabilistic_latent_semantic_analysis .
@prefix rdf: .
@prefix yago: .
dbr:Probabilistic_latent_semantic_analysis rdf:type yago:Person100007846 ,
yago:CausalAgent100007347 ,
yago:YagoLegalActorGeo ,
yago:YagoLegalActor ,
yago:Whole100003553 ,
yago:Assistant109815790 ,
yago:PhysicalEntity100001930 ,
yago:Object100002684 ,
yago:Model110324560 ,
yago:LivingThing100004258 ,
yago:Organism100004475 ,
yago:Worker109632518 ,
yago:WikicatLatentVariableModels ,
dbo:TopicalConcept .
@prefix rdfs: .
dbr:Probabilistic_latent_semantic_analysis rdfs:label "Probabilistic latent semantic analysis"@en ,
"An\u00E1lise Probabilistica de Sem\u00E2ntica Latente"@pt ,
"Analyse s\u00E9mantique latente probabiliste"@fr ,
"\u6982\u7387\u6F5C\u5728\u8BED\u4E49\u5206\u6790"@zh ,
"\u0412\u0435\u0440\u043E\u044F\u0442\u043D\u043E\u0441\u0442\u043D\u044B\u0439 \u043B\u0430\u0442\u0435\u043D\u0442\u043D\u043E-\u0441\u0435\u043C\u0430\u043D\u0442\u0438\u0447\u0435\u0441\u043A\u0438\u0439 \u0430\u043D\u0430\u043B\u0438\u0437"@ru ;
rdfs:comment "\u6982\u7387\u7684\u6F5C\u5728\u8BED\u4E49\u5206\u6790\uFF08PLSA\uFF09\uFF0C\u4E5F\u79F0\u4E3A\u6982\u7387\u6F5C\u5728\u8BED\u4E49\u7D22\u5F15\uFF08PLSI\uFF0C\u5C24\u5176\u662F\u5728\u4FE1\u606F\u68C0\u7D22\u9886\u57DF\uFF09\uFF0C\u662F\u7528\u4E8E\u5206\u6790\u53CC\u6A21\u548C\u5171\u73B0\u6570\u636E\u7684\u7EDF\u8BA1\u65B9\u6CD5\u3002 \u5B9E\u9645\u4E0A\uFF0C\u4EBA\u4EEC\u53EF\u4EE5\u6839\u636E\u5BF9\u67D0\u4E9B\u9690\u53D8\u91CF\u7684\u4EB2\u548C\u6027\u6765\u63A8\u5BFC\u51FA\u89C2\u6D4B\u53D8\u91CF\u7684\u4F4E\u7EF4\u8868\u793A\uFF0C\u5C31\u50CFPLSA\u662F\u4ECE\u6F5C\u5728\u8BED\u4E49\u5206\u6790\u4E2D\u6F14\u5316\u800C\u6765\u3002 \u4E0E\u6E90\u4E8E\u7EBF\u6027\u4EE3\u6570\u5E76\u7F29\u5C0F\u53D1\u751F\u8868\uFF08\u901A\u5E38\u901A\u8FC7\u5947\u5F02\u503C\u5206\u89E3\uFF09\u7684\u6807\u51C6\u6F5C\u5728\u8BED\u4E49\u5206\u6790\u6240\u4E0D\u540C\u7684\u662F\uFF0C\u6982\u7387\u6F5C\u5728\u8BED\u4E49\u5206\u6790\u57FA\u4E8E\u4ECE\u6F5C\u7C7B\u6A21\u578B\u5BFC\u51FA\u7684\u6DF7\u5408\u5206\u89E3\u3002"@zh ,
"\u0412\u0435\u0440\u043E\u044F\u0442\u043D\u043E\u0441\u0442\u043D\u044B\u0439 \u043B\u0430\u0442\u0435\u043D\u0442\u043D\u043E-\u0441\u0435\u043C\u0430\u043D\u0442\u0438\u0447\u0435\u0441\u043A\u0438\u0439 \u0430\u043D\u0430\u043B\u0438\u0437 (\u0412\u041B\u0421\u0410), \u0442\u0430\u043A\u0436\u0435 \u0438\u0437\u0432\u0435\u0441\u0442\u043D\u044B\u0439 \u043A\u0430\u043A \u0432\u0435\u0440\u043E\u044F\u0442\u043D\u043E\u0441\u0442\u043D\u043E\u0435 \u043B\u0430\u0442\u0435\u043D\u0442\u043D\u043E-\u0441\u0435\u043C\u0430\u043D\u0442\u0438\u0447\u0435\u0441\u043A\u043E\u0435 \u0438\u043D\u0434\u0435\u043A\u0441\u0438\u0440\u043E\u0432\u0430\u043D\u0438\u0435 (\u0412\u041B\u0421\u0418, \u043E\u0441\u043E\u0431\u0435\u043D\u043D\u043E \u0432 \u043E\u0431\u043B\u0430\u0441\u0442\u0438 \u0438\u043D\u0444\u043E\u0440\u043C\u0430\u0446\u0438\u043E\u043D\u043D\u043E\u0433\u043E \u043F\u043E\u0438\u0441\u043A\u0430) \u2014 \u044D\u0442\u043E \u0441\u0442\u0430\u0442\u0438\u0441\u0442\u0438\u0447\u0435\u0441\u043A\u0438\u0439 \u043C\u0435\u0442\u043E\u0434 \u0430\u043D\u0430\u043B\u0438\u0437\u0430 \u043A\u043E\u0440\u0440\u0435\u043B\u044F\u0446\u0438\u0438 \u0434\u0432\u0443\u0445 \u0442\u0438\u043F\u043E\u0432 \u0434\u0430\u043D\u043D\u044B\u0445. \u0414\u0430\u043D\u043D\u044B\u0439 \u043C\u0435\u0442\u043E\u0434 \u044F\u0432\u043B\u044F\u0435\u0442\u0441\u044F \u0434\u0430\u043B\u044C\u043D\u0435\u0439\u0448\u0438\u043C \u0440\u0430\u0437\u0432\u0438\u0442\u0438\u0435\u043C \u043B\u0430\u0442\u0435\u043D\u0442\u043D\u043E-\u0441\u0435\u043C\u0430\u043D\u0442\u0438\u0447\u0435\u0441\u043A\u043E\u0433\u043E \u0430\u043D\u0430\u043B\u0438\u0437\u0430. \u0412\u041B\u0421\u0410 \u043F\u0440\u0438\u043C\u0435\u043D\u044F\u0435\u0442\u0441\u044F \u0432 \u0442\u0430\u043A\u0438\u0445 \u043E\u0431\u043B\u0430\u0441\u0442\u044F\u0445 \u043A\u0430\u043A \u0438\u043D\u0444\u043E\u0440\u043C\u0430\u0446\u0438\u043E\u043D\u043D\u044B\u0439 \u043F\u043E\u0438\u0441\u043A, \u043E\u0431\u0440\u0430\u0431\u043E\u0442\u043A\u0430 \u0435\u0441\u0442\u0435\u0441\u0442\u0432\u0435\u043D\u043D\u043E\u0433\u043E \u044F\u0437\u044B\u043A\u0430, \u043C\u0430\u0448\u0438\u043D\u043D\u043E\u0435 \u043E\u0431\u0443\u0447\u0435\u043D\u0438\u0435 \u0438 \u0441\u043C\u0435\u0436\u043D\u044B\u0445 \u043E\u0431\u043B\u0430\u0441\u0442\u044F\u0445.\u0414\u0430\u043D\u043D\u044B\u0439 \u043C\u0435\u0442\u043E\u0434 \u0431\u044B\u043B \u0432\u043F\u0435\u0440\u0432\u044B\u0435 \u043E\u043F\u0443\u0431\u043B\u0438\u043A\u043E\u0432\u0430\u043D \u0432 1999 \u0433\u043E\u0434\u0443 \u0422\u043E\u043C\u0430\u0441\u043E\u043C \u0425\u043E\u0444\u043C\u0430\u043D\u043E\u043C (Thomas Hofmann)."@ru ,
"An\u00E1lise Probabil\u00EDstica de Sem\u00E2ntica Latente (APSL), tamb\u00E9m conhecida como Indexa\u00E7\u00E3o Probabil\u00EDstica de Sem\u00E2ntica Latente (IPSL, especialmente na \u00E1rea de recupera\u00E7\u00E3o de informa\u00E7\u00E3o) \u00E9 uma t\u00E9cnica estat\u00EDstica para a an\u00E1lise de co-ocorr\u00EAncia de dados. Em efeito, pode-se derivar uma representa\u00E7\u00E3o de poucas dimens\u00F5es das vari\u00E1veis observadas com rela\u00E7\u00E3o sua afinidade para determinadas vari\u00E1veis ocultas. A t\u00E9cnica evoluiu da an\u00E1lise de sem\u00E2ntica latente."@pt ,
"L\u2019analyse s\u00E9mantique latente probabiliste (de l'anglais, Probabilistic latent semantic analysis : PLSA), aussi appel\u00E9e indexation s\u00E9mantique latente probabiliste (PLSI), est une m\u00E9thode de traitement automatique des langues inspir\u00E9e de l'analyse s\u00E9mantique latente. Elle am\u00E9liore cette derni\u00E8re en incluant un mod\u00E8le statistique particulier. La PLSA poss\u00E8de des applications dans le filtrage et la recherche d'information, le traitement des langues naturelles, l'apprentissage automatique et les domaines associ\u00E9s."@fr ,
"Probabilistic latent semantic analysis (PLSA), also known as probabilistic latent semantic indexing (PLSI, especially in information retrieval circles) is a statistical technique for the analysis of two-mode and co-occurrence data. In effect, one can derive a low-dimensional representation of the observed variables in terms of their affinity to certain hidden variables, just as in latent semantic analysis, from which PLSA evolved."@en ;
foaf:depiction .
@prefix dcterms: .
@prefix dbc: .
dbr:Probabilistic_latent_semantic_analysis dcterms:subject dbc:Latent_variable_models ,
dbc:Statistical_natural_language_processing ,
dbc:Classification_algorithms ,
dbc:Language_modeling ;
dbo:abstract "Probabilistic latent semantic analysis (PLSA), also known as probabilistic latent semantic indexing (PLSI, especially in information retrieval circles) is a statistical technique for the analysis of two-mode and co-occurrence data. In effect, one can derive a low-dimensional representation of the observed variables in terms of their affinity to certain hidden variables, just as in latent semantic analysis, from which PLSA evolved. Compared to standard latent semantic analysis which stems from linear algebra and downsizes the occurrence tables (usually via a singular value decomposition), probabilistic latent semantic analysis is based on a mixture decomposition derived from a latent class model."@en ,
"L\u2019analyse s\u00E9mantique latente probabiliste (de l'anglais, Probabilistic latent semantic analysis : PLSA), aussi appel\u00E9e indexation s\u00E9mantique latente probabiliste (PLSI), est une m\u00E9thode de traitement automatique des langues inspir\u00E9e de l'analyse s\u00E9mantique latente. Elle am\u00E9liore cette derni\u00E8re en incluant un mod\u00E8le statistique particulier. La PLSA poss\u00E8de des applications dans le filtrage et la recherche d'information, le traitement des langues naturelles, l'apprentissage automatique et les domaines associ\u00E9s. Elle fut introduite en 1999 par , et poss\u00E8de des liens avec la factorisation de matrices positives. Compar\u00E9e \u00E0 l'analyse s\u00E9mantique latente simple, qui d\u00E9coule de l'alg\u00E8bre lin\u00E9aire pour r\u00E9duire les matrices des occurrences (au moyen d'une d\u00E9composition en valeurs singuli\u00E8res), l'approche probabiliste emploie un m\u00E9lange de d\u00E9compositions issues de l'analyse des classes latentes. On obtient ainsi une approche plus souple, fond\u00E9e sur les statistiques. Il a \u00E9t\u00E9 montr\u00E9 que l'analyse s\u00E9mantique latente probabiliste souffre parfois de surapprentissage, le nombre de param\u00E8tres croissant lin\u00E9airement avec celui des documents.Bien que PLSA soit un mod\u00E8le g\u00E9n\u00E9ratif des documents de la collection, elle mod\u00E9lise effectivement directement la densit\u00E9 jointe , elle ne permet pas de g\u00E9n\u00E9rer de nouveaux documents, et en ce sens n'est pas un \u00AB vrai \u00BB mod\u00E8le g\u00E9n\u00E9ratif. Cette limitation est lev\u00E9e par l'Allocation de Dirichlet latente (LDA)."@fr ,
"\u6982\u7387\u7684\u6F5C\u5728\u8BED\u4E49\u5206\u6790\uFF08PLSA\uFF09\uFF0C\u4E5F\u79F0\u4E3A\u6982\u7387\u6F5C\u5728\u8BED\u4E49\u7D22\u5F15\uFF08PLSI\uFF0C\u5C24\u5176\u662F\u5728\u4FE1\u606F\u68C0\u7D22\u9886\u57DF\uFF09\uFF0C\u662F\u7528\u4E8E\u5206\u6790\u53CC\u6A21\u548C\u5171\u73B0\u6570\u636E\u7684\u7EDF\u8BA1\u65B9\u6CD5\u3002 \u5B9E\u9645\u4E0A\uFF0C\u4EBA\u4EEC\u53EF\u4EE5\u6839\u636E\u5BF9\u67D0\u4E9B\u9690\u53D8\u91CF\u7684\u4EB2\u548C\u6027\u6765\u63A8\u5BFC\u51FA\u89C2\u6D4B\u53D8\u91CF\u7684\u4F4E\u7EF4\u8868\u793A\uFF0C\u5C31\u50CFPLSA\u662F\u4ECE\u6F5C\u5728\u8BED\u4E49\u5206\u6790\u4E2D\u6F14\u5316\u800C\u6765\u3002 \u4E0E\u6E90\u4E8E\u7EBF\u6027\u4EE3\u6570\u5E76\u7F29\u5C0F\u53D1\u751F\u8868\uFF08\u901A\u5E38\u901A\u8FC7\u5947\u5F02\u503C\u5206\u89E3\uFF09\u7684\u6807\u51C6\u6F5C\u5728\u8BED\u4E49\u5206\u6790\u6240\u4E0D\u540C\u7684\u662F\uFF0C\u6982\u7387\u6F5C\u5728\u8BED\u4E49\u5206\u6790\u57FA\u4E8E\u4ECE\u6F5C\u7C7B\u6A21\u578B\u5BFC\u51FA\u7684\u6DF7\u5408\u5206\u89E3\u3002"@zh ,
"\u0412\u0435\u0440\u043E\u044F\u0442\u043D\u043E\u0441\u0442\u043D\u044B\u0439 \u043B\u0430\u0442\u0435\u043D\u0442\u043D\u043E-\u0441\u0435\u043C\u0430\u043D\u0442\u0438\u0447\u0435\u0441\u043A\u0438\u0439 \u0430\u043D\u0430\u043B\u0438\u0437 (\u0412\u041B\u0421\u0410), \u0442\u0430\u043A\u0436\u0435 \u0438\u0437\u0432\u0435\u0441\u0442\u043D\u044B\u0439 \u043A\u0430\u043A \u0432\u0435\u0440\u043E\u044F\u0442\u043D\u043E\u0441\u0442\u043D\u043E\u0435 \u043B\u0430\u0442\u0435\u043D\u0442\u043D\u043E-\u0441\u0435\u043C\u0430\u043D\u0442\u0438\u0447\u0435\u0441\u043A\u043E\u0435 \u0438\u043D\u0434\u0435\u043A\u0441\u0438\u0440\u043E\u0432\u0430\u043D\u0438\u0435 (\u0412\u041B\u0421\u0418, \u043E\u0441\u043E\u0431\u0435\u043D\u043D\u043E \u0432 \u043E\u0431\u043B\u0430\u0441\u0442\u0438 \u0438\u043D\u0444\u043E\u0440\u043C\u0430\u0446\u0438\u043E\u043D\u043D\u043E\u0433\u043E \u043F\u043E\u0438\u0441\u043A\u0430) \u2014 \u044D\u0442\u043E \u0441\u0442\u0430\u0442\u0438\u0441\u0442\u0438\u0447\u0435\u0441\u043A\u0438\u0439 \u043C\u0435\u0442\u043E\u0434 \u0430\u043D\u0430\u043B\u0438\u0437\u0430 \u043A\u043E\u0440\u0440\u0435\u043B\u044F\u0446\u0438\u0438 \u0434\u0432\u0443\u0445 \u0442\u0438\u043F\u043E\u0432 \u0434\u0430\u043D\u043D\u044B\u0445. \u0414\u0430\u043D\u043D\u044B\u0439 \u043C\u0435\u0442\u043E\u0434 \u044F\u0432\u043B\u044F\u0435\u0442\u0441\u044F \u0434\u0430\u043B\u044C\u043D\u0435\u0439\u0448\u0438\u043C \u0440\u0430\u0437\u0432\u0438\u0442\u0438\u0435\u043C \u043B\u0430\u0442\u0435\u043D\u0442\u043D\u043E-\u0441\u0435\u043C\u0430\u043D\u0442\u0438\u0447\u0435\u0441\u043A\u043E\u0433\u043E \u0430\u043D\u0430\u043B\u0438\u0437\u0430. \u0412\u041B\u0421\u0410 \u043F\u0440\u0438\u043C\u0435\u043D\u044F\u0435\u0442\u0441\u044F \u0432 \u0442\u0430\u043A\u0438\u0445 \u043E\u0431\u043B\u0430\u0441\u0442\u044F\u0445 \u043A\u0430\u043A \u0438\u043D\u0444\u043E\u0440\u043C\u0430\u0446\u0438\u043E\u043D\u043D\u044B\u0439 \u043F\u043E\u0438\u0441\u043A, \u043E\u0431\u0440\u0430\u0431\u043E\u0442\u043A\u0430 \u0435\u0441\u0442\u0435\u0441\u0442\u0432\u0435\u043D\u043D\u043E\u0433\u043E \u044F\u0437\u044B\u043A\u0430, \u043C\u0430\u0448\u0438\u043D\u043D\u043E\u0435 \u043E\u0431\u0443\u0447\u0435\u043D\u0438\u0435 \u0438 \u0441\u043C\u0435\u0436\u043D\u044B\u0445 \u043E\u0431\u043B\u0430\u0441\u0442\u044F\u0445.\u0414\u0430\u043D\u043D\u044B\u0439 \u043C\u0435\u0442\u043E\u0434 \u0431\u044B\u043B \u0432\u043F\u0435\u0440\u0432\u044B\u0435 \u043E\u043F\u0443\u0431\u043B\u0438\u043A\u043E\u0432\u0430\u043D \u0432 1999 \u0433\u043E\u0434\u0443 \u0422\u043E\u043C\u0430\u0441\u043E\u043C \u0425\u043E\u0444\u043C\u0430\u043D\u043E\u043C (Thomas Hofmann). \u041F\u043E \u0441\u0440\u0430\u0432\u043D\u0435\u043D\u0438\u044E \u0441 \u043E\u0431\u044B\u0447\u043D\u044B\u043C \u043B\u0430\u0442\u0435\u043D\u0442\u043D\u043E-\u0441\u0435\u043C\u0430\u043D\u0442\u0438\u0447\u0435\u0441\u043A\u0438\u043C \u0430\u043D\u0430\u043B\u0438\u0437\u043E\u043C, \u043A\u043E\u0442\u043E\u0440\u044B\u0439 \u043E\u0441\u043D\u043E\u0432\u0430\u043D \u043D\u0430 \u043B\u0438\u043D\u0435\u0439\u043D\u043E\u0439 \u0430\u043B\u0433\u0435\u0431\u0440\u0435 \u0438 \u044F\u0432\u043B\u044F\u0435\u0442\u0441\u044F \u0441\u043F\u043E\u0441\u043E\u0431\u043E\u043C \u0441\u043D\u0438\u0436\u0435\u043D\u0438\u044F \u0440\u0430\u0437\u043C\u0435\u0440\u043D\u043E\u0441\u0442\u0438 \u043C\u0430\u0442\u0440\u0438\u0446\u044B (\u043A\u0430\u043A \u043F\u0440\u0430\u0432\u0438\u043B\u043E, \u0441 \u043F\u043E\u043C\u043E\u0449\u044C\u044E \u0440\u0430\u0437\u043B\u043E\u0436\u0435\u043D\u0438\u044F \u0434\u0438\u0430\u0433\u043E\u043D\u0430\u043B\u044C\u043D\u043E\u0439 \u043C\u0430\u0442\u0440\u0438\u0446\u044B \u043F\u043E \u0441\u0438\u043D\u0433\u0443\u043B\u044F\u0440\u043D\u044B\u043C \u0437\u043D\u0430\u0447\u0435\u043D\u0438\u044F\u043C), \u0432\u0435\u0440\u043E\u044F\u0442\u043D\u043E\u0441\u0442\u043D\u044B\u0439 \u043B\u0430\u0442\u0435\u043D\u0442\u043D\u043E-\u0441\u0435\u043C\u0430\u043D\u0442\u0438\u0447\u0435\u0441\u043A\u0438\u0439 \u0430\u043D\u0430\u043B\u0438\u0437 \u043E\u0441\u043D\u043E\u0432\u0430\u043D \u043D\u0430 \u0441\u043C\u0435\u0448\u0430\u043D\u043D\u043E\u043C \u0440\u0430\u0437\u043B\u043E\u0436\u0435\u043D\u0438\u0438, \u0432 \u0441\u0432\u043E\u044E \u043E\u0447\u0435\u0440\u0435\u0434\u044C \u0431\u0435\u0440\u0443\u0449\u0438\u043C \u0441\u0432\u043E\u0451 \u043D\u0430\u0447\u0430\u043B\u043E \u0438\u0437 \u043C\u043E\u0434\u0435\u043B\u0438 \u0441\u043A\u0440\u044B\u0442\u044B\u0445 \u043A\u043B\u0430\u0441\u0441\u043E\u0432. \u0414\u0430\u043D\u043D\u044B\u0439 \u043F\u043E\u0434\u0445\u043E\u0434 \u0431\u043E\u043B\u0435\u0435 \u043F\u0440\u0438\u043D\u0446\u0438\u043F\u0438\u0430\u043B\u0435\u043D, \u043F\u043E\u0441\u043A\u043E\u043B\u044C\u043A\u0443 \u0438\u043C\u0435\u0435\u0442 \u043F\u0440\u043E\u0447\u043D\u0443\u044E \u043E\u0441\u043D\u043E\u0432\u0443 \u0432 \u043E\u0431\u043B\u0430\u0441\u0442\u0438 \u0441\u0442\u0430\u0442\u0438\u0441\u0442\u0438\u043A\u0438."@ru ,
"An\u00E1lise Probabil\u00EDstica de Sem\u00E2ntica Latente (APSL), tamb\u00E9m conhecida como Indexa\u00E7\u00E3o Probabil\u00EDstica de Sem\u00E2ntica Latente (IPSL, especialmente na \u00E1rea de recupera\u00E7\u00E3o de informa\u00E7\u00E3o) \u00E9 uma t\u00E9cnica estat\u00EDstica para a an\u00E1lise de co-ocorr\u00EAncia de dados. Em efeito, pode-se derivar uma representa\u00E7\u00E3o de poucas dimens\u00F5es das vari\u00E1veis observadas com rela\u00E7\u00E3o sua afinidade para determinadas vari\u00E1veis ocultas. A t\u00E9cnica evoluiu da an\u00E1lise de sem\u00E2ntica latente. Comparado com a an\u00E1lise de sem\u00E2ntica latente padr\u00E3o que decorre de \u00E1lgebra linear e redimensionamento de matrizes (geralmente atrav\u00E9s de uma decomposi\u00E7\u00E3o em valores singulares), APSL \u00E9 baseada em uma decomposi\u00E7\u00E3o mista derivada de um modelo latente de classes."@pt ;
dbo:wikiPageWikiLink dbr:Overfitting ,
dbc:Classification_algorithms ,
dbr:Bioinformatics ,
dbr:Singular_value_decomposition ,
dbr:Information_retrieval ,
dbr:Statistical_technique ,
dbr:Dirichlet_distribution ,
,
dbr:Aspect_model ,
dbr:Natural_language_processing ,
dbr:Fisher_kernel ,
dbr:Machine_learning ,
dbc:Language_modeling ,
dbc:Statistical_natural_language_processing ,
dbr:Pachinko_allocation ,
dbc:Latent_variable_models ,
dbr:Latent_Dirichlet_allocation ,
dbr:EM_algorithm ,
dbr:Latent_class_model ,
dbr:Compound_term_processing ,
dbr:Vector_space_model ,
dbr:Linear_algebra ,
dbr:Multinomial_distribution ,
dbr:Latent_semantic_analysis ,
dbr:Information_filtering ,
dbr:Non-negative_matrix_factorization .
@prefix dbp: .
@prefix dbt: .
dbr:Probabilistic_latent_semantic_analysis dbp:wikiPageUsesTemplate dbt:Reflist ;
dbo:thumbnail ;
dbo:wikiPageRevisionID 1103977922 ;
dbo:wikiPageExternalLink ,
.
@prefix xsd: .
dbr:Probabilistic_latent_semantic_analysis dbo:wikiPageLength "7785"^^xsd:nonNegativeInteger ;
dbo:wikiPageID 2088675 .
@prefix owl: .
dbr:Probabilistic_latent_semantic_analysis owl:sameAs ,
,
,
,
,
,
dbr:Probabilistic_latent_semantic_analysis .
@prefix wikidata: .
dbr:Probabilistic_latent_semantic_analysis owl:sameAs wikidata:Q2845258 .
@prefix yago-res: .
dbr:Probabilistic_latent_semantic_analysis owl:sameAs yago-res:Probabilistic_latent_semantic_analysis ,
.
@prefix gold: .
dbr:Probabilistic_latent_semantic_analysis gold:hypernym dbr:Technique .
@prefix prov: .
dbr:Probabilistic_latent_semantic_analysis prov:wasDerivedFrom ;
foaf:isPrimaryTopicOf wikipedia-en:Probabilistic_latent_semantic_analysis .