Optimal Selection of Time-Frequency Representations for Signal Classification: a Kernel-Target Alignment Approach
Paul Honeiné, Cdric Richard, Patrick Flandrin, Jean‐Baptiste Pothin · 2006
In this paper, we propose a method for selecting time-frequency distributions appropriate for given learning tasks. It is based on a criterion that has recently emerged from the machine learning literature: the kernel-target alignment. This criterion makes possible to find the optimal representation for a given classification problem without designing the classifier itself. Some possible applications of our framework are discussed. The first one provides a computationally attractive way of adjusting the free parameters of a distribution to improve classification performance. The second one is related to the selection, from a set of candidates, of the distribution that best facilitates a classification task. The last one addresses the problem of optimally combining several distributions