PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Large Margin Filterin
Rémi Flamary, Devis Tuia, Gustavo Camps Valls and Alain Rakotomamonjy
IEEE Trans of Signal Processing Volume 60, Number 2, pp. 648-659, 2012.

Abstract

We address in this paper the problem of multi- channel signal sequence labeling. In particular, we consider the problem where the signals are contaminated by both additive and convolutional noise. We investigate several approaches based on windowing and propose to learn a support vector machine (SVM) classifier and a signal filter jointly. We derive algorithms to solve the optimization problem and discuss different filter regularizers for automated scaling or selection of channels. After considering its properties on a toy dataset, the approach is tested on two challenging real life datasets: BCI time series and 2-dimensional image segmentation. Results show the interest of large margin filtering in terms of performance and interpretability.

EPrint Type:Article
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Learning/Statistics & Optimisation
Brain Computer Interfaces
ID Code:9205
Deposited By:Alain Rakotomamonjy
Deposited On:21 February 2012