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Lun. 15/01/2018 11:00 K71, Bâtiment K, RdC

MEI Jiali (LMO, Université Paris-Sud, EDF R&D)
Nonnegative Matrix Factorization with Side Information for Time Series Recovery and Prediction

Sommaire:

Motivated by the reconstruction and prediction of electricity consumption, we extend Nonnegative Matrix Factorization (NMF) to take into account outside features.
We consider Nonnegative Matrix Factorization in general linear measurement schemes, and propose a general framework which models non-linear relationship between features and the response variables.
We extend previous theoretical results in NMF to obtain a sufficient condition on the identifability of matrix factorization.
Based the classical Hierarchical Alternating Least Squares (HALS) algorithm, we propose a new algorithm (HALSX, or Hierarchical Alternating Least Squares with eXogeneous variables) which estimates the factorization model.
The algorithm is validated on both simulated and real electricity consumption data, to show its performance in reconstruction and prediction.


Pour plus d'informations, merci de contacter Cugliari J.