Parameter Training of Wavelet Domain Hidden Markov Models Using the EM Algorithm

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Resource Overview

This approach utilizes the Expectation-Maximization (EM) algorithm for parameter training in wavelet domain Hidden Markov Models (HMMs), demonstrating improved efficiency in training time compared to alternative methods. The implementation involves iterative estimation of latent state probabilities and optimization of model parameters through maximum likelihood estimation.

Detailed Documentation

Parameter training for wavelet domain Hidden Markov Models using the EM algorithm is a widely adopted methodology. This technique enables more precise estimation of model parameters through iterative expectation (E-step) and maximization (M-step) phases, thereby enhancing model performance. During implementation, the E-step computes posterior probabilities of hidden states given observed wavelet coefficients, while the M-step updates transition probabilities and emission distributions using maximum likelihood estimation. Although this training approach may require more computational time compared to other methods, it effectively optimizes training outcomes by ensuring convergence to local maxima of the likelihood function, ultimately improving overall training efficacy and model robustness.

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