A new scheme for extracting multi-temporal sequence patterns
This paper proposes a new scheme for unsupervised multi-temporal sequence pattern extraction. The main idea of the scheme is iterative coarse to fine data examination. We decompose a pattern into ambiguous subpatterns and distinguishable sub-patterns (DSP). In each iteration, we coarsely examine the training temporal signal sequence by training an Elman neural network. The trained Elman network is used to select the DSP candidate set. Then, we look at the training signals around the DSPs and use maximum likelihood criteria to expand them into whole patterns. We cut out the new found patterns from the training signal sequence and repeat the whole procedure until no more new patterns are found. The experimental result shows this method promising.