A Probabilistic Semi-Supervised Approach with Triplet Markov Chains
Katherine Morales Yohan Petetin
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.