Essec\Faculty\Model\Contribution {#2216 ▼
#_index: "academ_contributions"
#_id: "13882"
#_source: array:26 [
"id" => "13882"
"slug" => "simultaneous-dimension-reduction-and-clustering-via-the-nmf-em-algorithm"
"yearMonth" => "2021-03"
"year" => "2021"
"title" => "Simultaneous dimension reduction and clustering via the NMF-EM algorithm"
"description" => "CAREL, L. et ALQUIER, P. (2021). Simultaneous dimension reduction and clustering via the NMF-EM algorithm. <i>Advances in Data Analysis and Classification</i>, 15(1), pp. 231-260. CAREL, L. et ALQUIER, P. (2021). Simultaneous dimension reduction and clustering via the NMF-EM algo "
"authors" => array:2 [
0 => array:3 [
"name" => "ALQUIER Pierre"
"bid" => "B00809923"
"slug" => "alquier-pierre"
]
1 => array:1 [
"name" => "CAREL Léna"
]
]
"ouvrage" => ""
"keywords" => []
"updatedAt" => "2023-03-22 09:28:55"
"publicationUrl" => "https://link.springer.com/article/10.1007/s11634-020-00398-4"
"publicationInfo" => array:3 [
"pages" => "231-260"
"volume" => "15"
"number" => "1"
]
"type" => array:2 [
"fr" => "Articles"
"en" => "Journal articles"
]
"support_type" => array:2 [
"fr" => "Revue scientifique"
"en" => "Scientific journal"
]
"countries" => array:2 [
"fr" => null
"en" => null
]
"abstract" => array:2 [
"fr" => "Mixture models are among the most popular tools for clustering. However, when the dimension and the number of clusters is large, the estimation of the clusters become challenging, as well as their interpretation. Restriction on the parameters can be used to reduce the dimension. An example is given by mixture of factor analyzers for Gaussian mixtures. The extension of MFA to non-Gaussian mixtures is not straightforward. We propose a new constraint for parameters in non-Gaussian mixture model: the K components parameters are combinations of elements from a small dictionary, say H elements, with H≪K. Including a nonnegative matrix factorization (NMF) in the EM algorithm allows us to simultaneously estimate the dictionary and the parameters of the mixture. We propose the acronym NMF-EM for this algorithm, implemented in the R package nmfem. This original approach is motivated by passengers clustering from ticketing data: we apply NMF-EM to data from two Transdev public transport networks. In this case, the words are easily interpreted as typical slots in a timetable. Mixture models are among the most popular tools for clustering. However, when the dimension and the "
"en" => "Mixture models are among the most popular tools for clustering. However, when the dimension and the number of clusters is large, the estimation of the clusters become challenging, as well as their interpretation. Restriction on the parameters can be used to reduce the dimension. An example is given by mixture of factor analyzers for Gaussian mixtures. The extension of MFA to non-Gaussian mixtures is not straightforward. We propose a new constraint for parameters in non-Gaussian mixture model: the K components parameters are combinations of elements from a small dictionary, say H elements, with H≪K. Including a nonnegative matrix factorization (NMF) in the EM algorithm allows us to simultaneously estimate the dictionary and the parameters of the mixture. We propose the acronym NMF-EM for this algorithm, implemented in the R package nmfem. This original approach is motivated by passengers clustering from ticketing data: we apply NMF-EM to data from two Transdev public transport networks. In this case, the words are easily interpreted as typical slots in a timetable. Mixture models are among the most popular tools for clustering. However, when the dimension and the "
]
"authors_fields" => array:2 [
"fr" => "Systèmes d'Information, Data Analytics et Opérations"
"en" => "Information Systems, Data Analytics and Operations"
]
"indexedAt" => "2025-01-17T15:21:42.000Z"
"docTitle" => "Simultaneous dimension reduction and clustering via the NMF-EM algorithm"
"docSurtitle" => "Journal articles"
"authorNames" => "<a href="/cv/alquier-pierre">ALQUIER Pierre</a>, CAREL Léna"
"docDescription" => "<span class="document-property-authors">ALQUIER Pierre, CAREL Léna</span><br><span class="document-property-authors_fields">Information Systems, Data Analytics and Operations</span> | <span class="document-property-year">2021</span> <span class="document-property-authors">ALQUIER Pierre, CAREL Léna</span><br><span class="document-p "
"keywordList" => ""
"docPreview" => "<b>Simultaneous dimension reduction and clustering via the NMF-EM algorithm</b><br><span>2021-03 | Journal articles </span> <b>Simultaneous dimension reduction and clustering via the NMF-EM algorithm</b><br><span>2021-03 | J "
"docType" => "research"
"publicationLink" => "<a href="https://link.springer.com/article/10.1007/s11634-020-00398-4" target="_blank">Simultaneous dimension reduction and clustering via the NMF-EM algorithm</a> <a href="https://link.springer.com/article/10.1007/s11634-020-00398-4" target="_blank">Simultaneous "
]
+lang: "en"
+"_type": "_doc"
+"_score": 8.966398
+"parent": null
}