Automatic temporal pattern extraction and association

This paper proposes an approach for automatic temporal pattern extraction from a temporal sequence, whose signal samples suffer from noise and non-linear temporal warping. The patterns are recurrent signal segments in the original temporal sequence with respect to some criteria defined by the user. We use Threshold hidden Markov model (THMM) to model the signal sequence. A variance of the Segmental K-means algorithm is used to train the THMM The trained THMM can be used to detect the pattern in the temporal sequence. We apply the proposed approach to automatically analyze multiple synchronized temporal sequences from several input modalities. The timing information of the extracted patterns is used to learn the correlation across multiple modalities, which leads to automatic concept learning.