Learning to extract temporal signal patterns from temporal signal sequence

We propose an approach that extracts patterns from a temporal signal sequence without prior knowledge about the lengths, positions and the number of the patterns. Previous research (Hong et al., 1999) proposes a scheme for extracting recurrent patterns from a noise free signal without temporal warping. To handle noise and nonlinear temporal warping, a threshold finite state machine (TFSM) is proposed to perform spatial-temporal data modeling. The TFSM is first roughly initialized. A variance of segmental K-means is used to train the TFSM. The training results give us both the patterns embedding in the signal sequence and the trained TFSM that can be used to represent and detect the patterns.