A Novel Algorithm for Unsupervised Behavioral Classification

I present a novel software package used for extraction of behavioral patterns from a data matrix with no a priori assumptions about the form of the data. The underlying algorithm transforms a data matrix into the time-frequency domain using a wavelet transform, which is analogous to taking a Fourier transform at each time step. This equalizes the power between frequency components, to ensure repeated high frequency motions do not dominate. The transformed matrix is then decomposed into behavioral patterns and their relative activity over time using an algorithm called seqNMF [1]. Though the algorithm was originally developed for positional data from an assay of Drosophila, it has been used to extract behavioral patterns from other organisms (e.g. E. coli) as well. The algorithm is robust with respect to both the number of underlying patterns in the data as well as the length of the patterns. In addition, the lack of any input other than the data itself makes the software package a powerful unsupervised classification tool with broad potential applications.
[1] E. L. Mackevicius, A. H. Bahle, A. H. Williams, S. Gu, N. I. Denissenko, M. S. Goldman, and M. S. Fee, Unsupervised Discovery of Temporal Sequences in High-Dimensional Datasets, with Applications to Neuroscience (2018).