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.
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Signoretto, M., De Lathauwer, L., Suykens, J.A.K. (2010). Kernel-Based Learning from Infinite Dimensional 2-Way Tensors. In: Diamantaras, K., Duch, W., Iliadis, L.S. (eds) Artificial Neural Networks – ICANN 2010. ICANN 2010. Lecture Notes in Computer Science, vol 6353. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15822-3_7
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DOI: https://doi.org/10.1007/978-3-642-15822-3_7
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-15821-6
Online ISBN: 978-3-642-15822-3
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