Time Signal Classification Using Random Convolutional Features

In this paper we present a transformation to convert time signals into a randomized low-dimensional vectors such that the inner product between these new features provides information about the similarity of the signals. We show that the described inner product approximates a cross-correlation based kernel. This is very useful at the moment of use Kernel Machines, such as Non-linear Support Vector Machines. Indeed, this allows to apply simpler and faster linear methods on the generated random features. Our proposed scheme improves computational storage and time cost over the direct kernel approach, while performing the classification performance with minimal loss. We support our statements by providing theoretical guarantees as well as empirical evaluation across different data sets.

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