A Probabilistic Semi-Supervised Approach with Triplet Markov Chains

TL;DR

This paper introduces a variational Bayesian semi-supervised learning framework for triplet Markov chain models, enabling effective training with limited labeled data for sequential Bayesian classification.

Contribution

It presents a novel general semi-supervised variational Bayesian approach for training triplet Markov chain models, adaptable to various sequential generative models.

Findings

Effective semi-supervised training with limited labels

Versatile framework applicable to multiple sequential models

Improved classification performance in sequential data

Abstract

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.