Aller au contenu principal

Thi Phuong Thao Tran

Vendredi 18 Septembre 2020

Interpretable time series kernel analytics by pre-image estimation.

Kernel methods are known to be effective to analyse complex objects by implicitly embedding them into some feature space. To interpret and analyse the obtained results, it is often required to restore in the input space the results obtained in the feature space by using pre-image estimation methods. This work proposes a pre-image estimation method for time series kernel analytics that consists of two steps. In the first step, a time warp function, driven by distance constraints in the feature space, is defined to embed time series in a metric space where analytics can be performed conveniently. In the second step, the time series pre-image estimation is cast as learning a linear (or a nonlinear) transformation that ensures a local isometry between the time series embedding space and the feature space.The proposed method is compared to state of the art through three major tasks that require pre-image estimation: 1) time series averaging, 2) time series reconstruction and denoising, and 3) time series representation learning. The extensive experiments conducted on 33 publicly-available datasets show the benefits of the pre-image estimation for time series kernel analytics.

Date et Lieu

Vendredi 18 Septembre à 14h00
Salle de réunion 306 - Bâtiment IMAG

Organisé par

Thi Phuong THAO TRAN
Equipe AMA

Composition du Jury

Mohamed NADIF
Professor, Paris Descartes University, Reviewer
Christophe MARSALA
Professor, Sorbonne University, Reviewer
Patrick GALLINARI
Professor, Sorbonne University, Examiner
Sihem AMER-YAHIA
Research director CNRS, Grenoble Alpes University, Examiner
Ahlame DOUZAL
Assistant Professor,  Grenoble Alpes University, Thesis director
Paul HONEINE
Professor, University of Rouen Normandie, Thesis co-director

Publié le 15 septembre 2020

Mis à jour le 28 décembre 2020