Determining temporal patterns in sensed data sequences by hierarchical decomposition of hidden Markov models
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United States of America Patent
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Jun 2, 2009
Grant Date -
Nov 17, 2005
app pub date -
May 12, 2004
filing date -
May 12, 2004
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Abstract
A method determines temporal patterns in data sequences. A hierarchical tree of nodes is constructed. Each node in the tree is associated with a composite hidden Markov model, in which the composite hidden Markov model has one independent path for each child node of a parent node of the hierarchical tree. The composite hidden Markov models are trained using training data sequences. The composite hidden Markov models associated with the nodes of the hierarchical tree are decomposed into a single final composite Markov model. The single final composite hidden Markov model can then be employed for determining temporal patterns in unknown data sequences.
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