Functional Regularized Least Squares Classi cation with Operator-valued KernelsReport as inadecuate




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1 SEQUEL - Sequential Learning LIFL - Laboratoire d-Informatique Fondamentale de Lille, LAGIS - Laboratoire d-Automatique, Génie Informatique et Signal, Inria Lille - Nord Europe 2 IMS - Laboratoire de l-intégration, du matériau au système 3 LIFL - Laboratoire d-Informatique Fondamentale de Lille 4 LAGIS-SI LAGIS - Laboratoire d-Automatique, Génie Informatique et Signal 5 LITIS - Laboratoire d-Informatique, de Traitement de l-Information et des Systèmes

Abstract : Although operator-valued kernels have recently received increasing interest in various machine learning and functional data analysis problems such as multi-task learning or functional regression, little attention has been paid to the understanding of their associated feature spaces. In this paper, we explore the potential of adopting an operator-valued kernel feature space perspective for the analysis of functional data. We then extend the Regularized Least Squares Classification RLSC algorithm to cover situations where there are multiple functions per observation. Experiments on a sound recognition problem show that the proposed method outperforms the classical RLSC algorithm.





Author: Hachem Kadri - Asma Rabaoui - Philippe Preux - Emmanuel Duflos - Alain Rakotomamonjy -

Source: https://hal.archives-ouvertes.fr/



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