Learning to Extract Temporal Signal Patterns from Temporal Signal Sequence
نویسندگان
چکیده
In this paper, we propose an approach that extracts patterns from a temporal signal sequence without knowing the lengths, positions and the number of the patterns. The temporal signal sequence consists of both regular pattern signals and random non-pattern signals. Previous research[3] proposes a scheme for extracting recurrent patterns from noise free signal without temporal warping. To handle noise and non-linear temporal warping, Threshold Finite State Machine (TFSM) is introduced. Scale Peeling Process is proposed to train the TFSM. The training results immediately give out the patterns embedding in the signal sequence and the trained TFSMs that can be used to represent and detect the sub-patterns.
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