@prefix owl: <http://www.w3.org/2002/07/owl#> .
@prefix skos: <http://www.w3.org/2004/02/skos/core#> .
@prefix inist: <http://www.inist.fr/Ontology#> .
@prefix dc: <http://purl.org/dc/terms/> .
@prefix xsd: <http://www.w3.org/2001/XMLSchema#> .

<http://data.loterre.fr/ark:/67375/8LP> a owl:Ontology, skos:ConceptScheme .
<http://data.loterre.fr/ark:/67375/8LP-Q2KJLRXR-Z>
  skos:prefLabel "sélection d'attributs"@fr, "feature selection"@en ;
  a skos:Concept ;
  skos:narrower <http://data.loterre.fr/ark:/67375/8LP-TRRMDS7R-R> .

<http://data.loterre.fr/ark:/67375/8LP-TRRMDS7R-R>
  inist:definitionalContext "Principal components analysis (PCA) is a method of dimensionality reduction. (Popescu & Dinu, 2009)"@en, "L'Analyse en Composantes Principales est une méthode descriptive d'analyse de données qui permet une étude simultanée de plus de 2 dimensions (analyse multivariée). (Duniec, Crouzet & Delais-Roussarie, 2020)"@fr ;
  skos:example "Cette matrice de modulations d'amplitude est alors transférée vers un outil statistique d'analyse en composantes principales (Principal Components Analysis). (Duniec, Crouzet & Delais-Roussarie, 2020)"@fr, "We apply PCA (Principal Component Analysis) to reduce the dimensionality of sentence embeddings to represent them in a two-dimensional space. (Niu, Xiong, Wang, Yu, Zhang & Yang, 2023)"@en, "Before explaining ICA we briefly explain PCA widely used for dimensionality reduction and whitening or sphering of feature vectors. (Yamagiwa, Oyama & Shimodaira, 2023)"@en, "We experimented with principal component analysis (PCA) and t-SNE (Van der Maaten and Hinton 2008) and found that at DMscale dimensionality these took too long and were too computationally intensive to resolve a query in web-appropriate time. (Sayeed, Hong & Demberg, 2016)"@en, "To tackle this problem we utilize PCA (Principal Component Analysis) to get variances along each axes which are represented by eigenvalues of covariance matrix subtracted the mean of each latent vectors. (Zou, Li, Liu & Deng, 2018)"@en, "Les plongements linguistiques de mots correspondent à la combinaison par analyse en composante principale de différents types de plongement de mots : word2vecf (Levy & Goldberg 2014) skipgram fournis par word2vec (Mikolov et al. 2013) et GloVe (Pennington et al. 2014) comme décrit dans (Ghannay et al. 2016). (Edwin Simonnet, Sahar Ghannay, Nathalie Camelin & Yannick Estève, 2018)"@fr, "A principal components analysis can also be performed on the selected features to see if they reflect any plausible semantic space. (Murphy, Baroni & Poesio, 2009)"@en ;
  skos:hiddenLabel "Principal component analysis"@en, "Analyse en composantes principales"@fr ;
  skos:prefLabel "analyse en composantes principales"@fr, "principal component analysis"@en ;
  a skos:Concept ;
  dc:modified "2024-07-01T07:28:33"^^xsd:dateTime ;
  skos:broader <http://data.loterre.fr/ark:/67375/8LP-Q2KJLRXR-Z> ;
  skos:inScheme <http://data.loterre.fr/ark:/67375/8LP> ;
  skos:altLabel "PCA"@fr, "PCA"@en, "principal components analysis"@en .