Hidden Markov Models with Momentum

TL;DR

This paper explores the impact of incorporating momentum into the Baum-Welch algorithm for Hidden Markov Models, showing it accelerates initial convergence but does not enhance final accuracy.

Contribution

It introduces and evaluates the use of momentum in Baum-Welch training for HMMs, a novel approach not previously studied.

Findings

Momentum reduces initial convergence time.

Final model performance remains unchanged with momentum.

Effectiveness varies with convergence speed.

Abstract

Momentum is a popular technique for improving convergence rates during gradient descent. In this research, we experiment with adding momentum to the Baum-Welch expectation-maximization algorithm for training Hidden Markov Models. We compare discrete Hidden Markov Models trained with and without momentum on English text and malware opcode data. The effectiveness of momentum is determined by measuring the changes in model score and classification accuracy due to momentum. Our extensive experiments indicate that adding momentum to Baum-Welch can reduce the number of iterations required for initial convergence during HMM training, particularly in cases where the model is slow to converge. However, momentum does not seem to improve the final model performance at a high number of iterations.