Feedforward Factorial Hidden Markov Model

This paper introduces a novel kind of factorial hidden Markov model (FHMM), specifically the feedforward FHMM (FFHMM). In contrast to traditional FHMMs, the FFHMM is capable of directly utilizing supplementary information from observations through predefined states, which are derived using an automatic feature filter (AFF). We investigate two variations of FFHMM models that integrate predefined states with the FHMM: the direct FFHMM and the embedded FFHMM. In the direct FFHMM, alterations to one sub-hidden Markov model (HMM) do not affect the others, enabling individual improvements in HMM estimation. On the other hand, the sub-HMM chains within the embedded FFHMM are interconnected, suggesting that adjustments to one HMM chain may enhance the estimations of other HMM chains. Consequently, we propose two algorithms for these FFHMM models to estimate their respective hidden states. Ultimately, experiments conducted on two real-world datasets validate the efficacy of the proposed models and algorithms.