Spatio-temporal Pattern Classification with KernelCanvas and WiSARD

Abstract

This work proposes a new method, KernelCanvas, that is adequate to the Weightless Neural Model known as WiSARD for generating a fixed length binary input from spatiotemporal patterns. The method, based on kernel distances, is simple to implement and scales linearly to the number of kernels. Five different datasets were used to evaluate its performance in comparison with more widely employed approaches. One dataset was related to human movements, two to handwriteen characters, one to speaker recognition and the last one to speech recognition. The KernelCanvas combined with WiSARD classifier approach frequently achieved the highest scores, sometimes losing only for the much slower K-Nearest Neighbors approach. In comparison with other results in the literature, our model has performed better or very close to them in all datasets.

A bag of nodes primer on weightless graph classification

“…Histogram Binarizationa thermometer encoding of the distribution histogram, where each histogram bin is a thermometer encoding of its min-max normalized value. This encoding scheme was previously used with good results in [6,12] for example.…”

“…KernelCanvas [12] Binarizationan R n division strategy that splits the space in k regions, being k defined as the number of kernels. Kernels are random points that when in R 2 produce a space partition similar to a Voronoi diagram.…”