Essec\Faculty\Model\Contribution {#2216
  #_index: "academ_contributions"
  #_id: "13904"
  #_source: array:26 [
    "id" => "13904"
    "slug" => "bayesian-methods-for-low-rank-matrix-estimation-short-survey-and-theoretical-study"
    "yearMonth" => "2013-10"
    "year" => "2013"
    "title" => "Bayesian Methods for Low-Rank Matrix Estimation: Short Survey and Theoretical Study"
    "description" => "ALQUIER, P. (2013). Bayesian Methods for Low-Rank Matrix Estimation: Short Survey and Theoretical Study. Dans: <i>24th International Conference on Algorithmic Learning Theory (ALT'13)</i>. Singapore:  Springer Berlin Heidelberg, pp. 309-323."
    "authors" => array:1 [
      0 => array:3 [
        "name" => "ALQUIER Pierre"
        "bid" => "B00809923"
        "slug" => "alquier-pierre"
      ]
    ]
    "ouvrage" => "24th International Conference on Algorithmic Learning Theory (ALT'13)"
    "keywords" => []
    "updatedAt" => "2023-03-22 13:58:49"
    "publicationUrl" => "https://link.springer.com/chapter/10.1007/978-3-642-40935-6_22"
    "publicationInfo" => array:3 [
      "pages" => "309-323"
      "volume" => ""
      "number" => ""
    ]
    "type" => array:2 [
      "fr" => "Actes d'une conférence"
      "en" => "Conference Proceedings"
    ]
    "support_type" => array:2 [
      "fr" => "Editeur"
      "en" => "Publisher"
    ]
    "countries" => array:2 [
      "fr" => null
      "en" => null
    ]
    "abstract" => array:2 [
      "fr" => "he problem of low-rank matrix estimation recently received a lot of attention due to challenging applications. A lot of work has been done on rank-penalized methods and convex relaxation, both on the theoretical and applied sides. However, only a few papers considered Bayesian estimation. In this paper, we review the different type of priors considered on matrices to favour low-rank. We also prove that the obtained Bayesian estimators, under suitable assumptions, enjoys the same optimality properties as the ones based on penalization."
      "en" => "he problem of low-rank matrix estimation recently received a lot of attention due to challenging applications. A lot of work has been done on rank-penalized methods and convex relaxation, both on the theoretical and applied sides. However, only a few papers considered Bayesian estimation. In this paper, we review the different type of priors considered on matrices to favour low-rank. We also prove that the obtained Bayesian estimators, under suitable assumptions, enjoys the same optimality properties as the ones based on penalization."
    ]
    "authors_fields" => array:2 [
      "fr" => "Systèmes d'Information, Data Analytics et Opérations"
      "en" => "Information Systems, Data Analytics and Operations"
    ]
    "indexedAt" => "2024-12-22T01:21:46.000Z"
    "docTitle" => "Bayesian Methods for Low-Rank Matrix Estimation: Short Survey and Theoretical Study"
    "docSurtitle" => "Conference Proceedings"
    "authorNames" => "<a href="/cv/alquier-pierre">ALQUIER Pierre</a>"
    "docDescription" => "<span class="document-property-authors">ALQUIER Pierre</span><br><span class="document-property-authors_fields">Information Systems, Data Analytics and Operations</span> | <span class="document-property-year">2013</span>"
    "keywordList" => ""
    "docPreview" => "<b>Bayesian Methods for Low-Rank Matrix Estimation: Short Survey and Theoretical Study</b><br><span>2013-10 | Conference Proceedings </span>"
    "docType" => "research"
    "publicationLink" => "<a href="https://link.springer.com/chapter/10.1007/978-3-642-40935-6_22" target="_blank">Bayesian Methods for Low-Rank Matrix Estimation: Short Survey and Theoretical Study</a>"
  ]
  +lang: "en"
  +"_type": "_doc"
  +"_score": 8.982854
  +"parent": null
}