Signal-Dependent Time-Frequency Representations for Classification using a Radially Gaussian Kernel and the Alignment Criterion

摘要

In this paper, we propose a method for tuning time-frequency distributions with radially Gaussian kernel within a classification framework. It is based on a criterion that has recently emerged from the machine learning literature: the kernel-target alignement. Our optimization scheme is very similar to that proposed by Baraniuk and Jones for signal-dependent time-frequency analysis. The relevance of this approach of improving time-frequency classification accuracy is illustrated through examples.