Scaling Factorial Hidden Markov Models: Stochastic Variational Inference without Messages
This work addresses a computational bottleneck for researchers and practitioners using FHMMs in sequential data analysis, though it is incremental as it builds on existing stochastic variational inference techniques.
The authors tackled the scalability issue of Factorial Hidden Markov Models (FHMMs) with long sequences by proposing a new algorithm that avoids message passing and can be distributed, achieving better performance without additional approximation bias compared to existing methods.
Factorial Hidden Markov Models (FHMMs) are powerful models for sequential data but they do not scale well with long sequences. We propose a scalable inference and learning algorithm for FHMMs that draws on ideas from the stochastic variational inference, neural network and copula literatures. Unlike existing approaches, the proposed algorithm requires no message passing procedure among latent variables and can be distributed to a network of computers to speed up learning. Our experiments corroborate that the proposed algorithm does not introduce further approximation bias compared to the proven structured mean-field algorithm, and achieves better performance with long sequences and large FHMMs.