Essec\Faculty\Model\Contribution {#2216
  #_index: "academ_contributions"
  #_id: "13911"
  #_source: array:26 [
    "id" => "13911"
    "slug" => "transductive-versions-of-the-lasso-and-the-dantzig-selector"
    "yearMonth" => "2012-09"
    "year" => "2012"
    "title" => "Transductive versions of the LASSO and the Dantzig Selector"
    "description" => "ALQUIER, P. et HEBIRI, M. (2012). Transductive versions of the LASSO and the Dantzig Selector. <i>Journal of Statistical Planning and Inference</i>, 142(9), pp. 2485-2500."
    "authors" => array:2 [
      0 => array:3 [
        "name" => "ALQUIER Pierre"
        "bid" => "B00809923"
        "slug" => "alquier-pierre"
      ]
      1 => array:1 [
        "name" => "HEBIRI Mohamed"
      ]
    ]
    "ouvrage" => ""
    "keywords" => array:6 [
      0 => "High-dimensional data"
      1 => "LASSO"
      2 => "Sparsity"
      3 => "High-dimensional regression estimation"
      4 => "Variable selection"
      5 => "Transduction"
    ]
    "updatedAt" => "2023-03-22 10:24:37"
    "publicationUrl" => "https://www.sciencedirect.com/science/article/pii/S037837581200136X"
    "publicationInfo" => array:3 [
      "pages" => "2485-2500"
      "volume" => "142"
      "number" => "9"
    ]
    "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" => "Transductive methods are useful in prediction problems when the training dataset is composed of a large number of unlabeled observations and a smaller number of labeled observations. In this paper, we propose an approach for developing transductive prediction procedures that are able to take advantage of the sparsity in the high dimensional linear regression. More precisely, we define transductive versions of the LASSO (Tibshirani, 1996) and the Dantzig Selector (Candès and Tao, 2007). These procedures combine labeled and unlabeled observations of the training dataset to produce a prediction for the unlabeled observations. We propose an experimental study of the transductive estimators that shows that they improve the LASSO and Dantzig Selector in many situations, and particularly in high dimensional problems when the predictors are correlated. We then provide non-asymptotic theoretical guarantees for these estimation methods. Interestingly, our theoretical results show that the Transductive LASSO and Dantzig Selector satisfy sparsity inequalities under weaker assumptions than those required for the “original” LASSO."
      "en" => "Transductive methods are useful in prediction problems when the training dataset is composed of a large number of unlabeled observations and a smaller number of labeled observations. In this paper, we propose an approach for developing transductive prediction procedures that are able to take advantage of the sparsity in the high dimensional linear regression. More precisely, we define transductive versions of the LASSO (Tibshirani, 1996) and the Dantzig Selector (Candès and Tao, 2007). These procedures combine labeled and unlabeled observations of the training dataset to produce a prediction for the unlabeled observations. We propose an experimental study of the transductive estimators that shows that they improve the LASSO and Dantzig Selector in many situations, and particularly in high dimensional problems when the predictors are correlated. We then provide non-asymptotic theoretical guarantees for these estimation methods. Interestingly, our theoretical results show that the Transductive LASSO and Dantzig Selector satisfy sparsity inequalities under weaker assumptions than those required for the “original” LASSO."
    ]
    "authors_fields" => array:2 [
      "fr" => "Systèmes d'Information, Data Analytics et Opérations"
      "en" => "Information Systems, Data Analytics and Operations"
    ]
    "indexedAt" => "2024-11-21T09:21:53.000Z"
    "docTitle" => "Transductive versions of the LASSO and the Dantzig Selector"
    "docSurtitle" => "Journal articles"
    "authorNames" => "<a href="/cv/alquier-pierre">ALQUIER Pierre</a>, HEBIRI Mohamed"
    "docDescription" => "<span class="document-property-authors">ALQUIER Pierre, HEBIRI Mohamed</span><br><span class="document-property-authors_fields">Information Systems, Data Analytics and Operations</span> | <span class="document-property-year">2012</span>"
    "keywordList" => "<a href="#">High-dimensional data</a>, <a href="#">LASSO</a>, <a href="#">Sparsity</a>, <a href="#">High-dimensional regression estimation</a>, <a href="#">Variable selection</a>, <a href="#">Transduction</a>"
    "docPreview" => "<b>Transductive versions of the LASSO and the Dantzig Selector</b><br><span>2012-09 | Journal articles </span>"
    "docType" => "research"
    "publicationLink" => "<a href="https://www.sciencedirect.com/science/article/pii/S037837581200136X" target="_blank">Transductive versions of the LASSO and the Dantzig Selector</a>"
  ]
  +lang: "en"
  +"_type": "_doc"
  +"_score": 8.554104
  +"parent": null
}