efficient and effective learning of hmms based on identification of hidden states

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ID: 203003
2017
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Abstract
The predominant learning algorithm for Hidden Markov Models (HMMs) is local search heuristics, of which the Baum-Welch (BW) algorithm is mostly used. It is an iterative learning procedure starting with a predefined size of state spaces and randomly chosen initial parameters. However, wrongly chosen initial parameters may cause the risk of falling into a local optimum and a low convergence speed. To overcome these drawbacks, we propose to use a more suitable model initialization approach, a Segmentation-Clustering and Transient analysis (SCT) framework, to estimate the number of states and model parameters directly from the input data. Based on an analysis of the information flow through HMMs, we demystify the structure of models and show that high-impact states are directly identifiable from the properties of observation sequences. States having a high impact on the log-likelihood make HMMs highly specific. Experimental results show that even though the identification accuracy drops to 87.9% when random models are considered, the SCT method is around 50 to 260 times faster than the BW algorithm with 100% correct identification for highly specific models whose specificity is greater than 0.06.
Reference Key
liu2017mathematicalefficient Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors ;Tingting Liu;Jan Lemeire
Journal journal of power sources
Year 2017
DOI
10.1155/2017/7318940
URL
http://dx.doi.org/10.1155/2017/7318940
Keywords
civil engineering (general)mathematics engineering (general)

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