Kernel-based learning from infinite dimensional 2-way tensors

Authors:
Marco Signoretto;Lieven De Lathauwer;Johan A. K. Suykens
Affiliations:
Katholieke Universiteit Leuven, ESAT, SCD, SISTA, Leuven, Belgium;Group Science, Engineering and Technology, Katholieke Universiteit Leuven, Kortrijk, Belgium;Katholieke Universiteit Leuven, ESAT, SCD, SISTA, Leuven, Belgium
Venue:
ICANN'10 Proceedings of the 20th international conference on Artificial neural networks: Part II
Year:
2010

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Abstract

In this paper we elaborate on a kernel extension to tensor-based data analysis. The proposed ideas find applications in supervised learning problems where input data have a natural 2-way representation, such as images or multivariate time series. Our approach aims at relaxing linearity of standard tensor-based analysis while still exploiting the structural information embodied in the input data.