Online Matrix Completion Through Nuclear Norm Regularisation

Charanpal Dhanjal 1, * Romaric Gaudel 2, 3 Stéphan Clémençon 1
* Auteur correspondant
3 SEQUEL - Sequential Learning
LIFL - Laboratoire d'Informatique Fondamentale de Lille, Inria Lille - Nord Europe, LAGIS - Laboratoire d'Automatique, Génie Informatique et Signal
Abstract : It is the main goal of this paper to propose a novel method to perform matrix completion on-line. Motivated by a wide variety of applications, ranging from the design of recommender systems to sensor network localization through seismic data reconstruction, we consider the matrix completion problem when entries of the matrix of interest are observed gradually. Precisely, we place ourselves in the situation where the predictive rule should be refined incrementally, rather than recomputed from scratch each time the sample of observed entries increases. The extension of existing matrix completion methods to the sequential prediction context is indeed a major issue in the Big Data era, and yet little addressed in the literature. The algorithm promoted in this article builds upon the Soft Impute approach introduced in Mazumder et al. (2010). The major novelty essentially arises from the use of a randomised technique for both computing and updating the Singular Value Decomposition (SVD) involved in the algorithm. Though of disarming simplicity, the method proposed turns out to be very efficient, while requiring reduced computations. Several numerical experiments based on real datasets illustrating its performance are displayed, together with preliminary results giving it a theoretical basis.
Type de document :
Communication dans un congrès
SDM - SIAM International Conference on Data Mining, Apr 2014, Philadelphia, United States. 2014, 〈http://epubs.siam.org/doi/abs/10.1137/1.9781611973440.72〉. 〈10.1137/1.9781611973440.72〉
Liste complète des métadonnées

Littérature citée [27 références]  Voir  Masquer  Télécharger

https://hal.inria.fr/hal-00926605
Contributeur : Charanpal Dhanjal <>
Soumis le : vendredi 10 janvier 2014 - 17:43:47
Dernière modification le : jeudi 11 janvier 2018 - 06:23:38
Document(s) archivé(s) le : vendredi 11 avril 2014 - 03:10:26

Fichiers

MatrixCompletionArxiv.pdf
Fichiers produits par l'(les) auteur(s)

Identifiants

Citation

Charanpal Dhanjal, Romaric Gaudel, Stéphan Clémençon. Online Matrix Completion Through Nuclear Norm Regularisation. SDM - SIAM International Conference on Data Mining, Apr 2014, Philadelphia, United States. 2014, 〈http://epubs.siam.org/doi/abs/10.1137/1.9781611973440.72〉. 〈10.1137/1.9781611973440.72〉. 〈hal-00926605v2〉

Partager

Métriques

Consultations de la notice

845

Téléchargements de fichiers

516