A Probabilistic Semi-Supervised Approach with Triplet Markov Chains
This work addresses a practical limitation in using triplet Markov chains for sequential data analysis, offering a semi-supervised solution that could benefit researchers and practitioners in fields like signal processing or bioinformatics, though it appears incremental as it extends existing variational methods to a specific model type.
The paper tackles the problem of training parameterized triplet Markov chain models when not all labels are available, proposing a general variational Bayesian inference framework for semi-supervised learning. The result is a method that enables semi-supervised algorithms for various generative models in sequential Bayesian classification.
Triplet Markov chains are general generative models for sequential data which take into account three kinds of random variables: (noisy) observations, their associated discrete labels and latent variables which aim at strengthening the distribution of the observations and their associated labels. However, in practice, we do not have at our disposal all the labels associated to the observations to estimate the parameters of such models. In this paper, we propose a general framework based on a variational Bayesian inference to train parameterized triplet Markov chain models in a semi-supervised context. The generality of our approach enables us to derive semi-supervised algorithms for a variety of generative models for sequential Bayesian classification.