1.2 On the regularization of Sliced Inverse Regression (Stéphane Girard)

Contenu fourni par Universite Paris 1 Pantheon-Sorbonne. Tout le contenu du podcast, y compris les épisodes, les graphiques et les descriptions de podcast, est téléchargé et fourni directement par Universite Paris 1 Pantheon-Sorbonne ou son partenaire de plateforme de podcast. Si vous pensez que quelqu'un utilise votre œuvre protégée sans votre autorisation, vous pouvez suivre le processus décrit ici https://fr.player.fm/legal.

StatLearn 2010 - Workshop on "Challenging problems in Statistical Learning"
1.2 On the regularization of Sliced Inverse Regression (Stéphane Girard)

9+ y ago 49:19

MP4•Maison d'episode

Série archivée ("Flux inactif" status)

When? This feed was archived on June 29, 2023 09:11 (10M ago). Last successful fetch was on August 01, 2022 18:06 (1+ y ago)

Why? Flux inactif status. Nos serveurs ont été incapables de récupérer un flux de podcast valide pour une période prolongée.

What now? You might be able to find a more up-to-date version using the search function. This series will no longer be checked for updates. If you believe this to be in error, please check if the publisher's feed link below is valid and contact support to request the feed be restored or if you have any other concerns about this.

Sliced Inverse Regression (SIR) is an effective method for dimension reduction in highdimensional regression problems. The original method, however, requires the inversion of the predictors covariance matrix. In case of collinearity between these predictors or small sample sizes compared to the dimension, the inversion is not possible and a regularization technique has to be used. Our approach is based on an interpretation of SIR axes as solutions of an inverse regression problem. A prior distribution is then introduced on the unknown parameters of the inverse regression problem in order to regularize their estimation. We show that some existing SIR regularizations can enter our framework, which permits a global understanding of these methods. Three new priors are proposed, leading to new regularizations of the SIR method, and compared on simulated data. An application to the estimation of Mars surface physical properties from hyperspectral images is provided.

12 episodes

#Éducation #Universite Paris 1 Pantheon-Sorbonne #Vidéo #Enseignement Supérieur