Subsampling-based HMC parameter estimation with application to large datasets classification

This paper presents a contextual algorithm for the approximation of Baum's forward and backward probabilities, which are extensively used in the framework of Hidden Markov chain models for parameter estimation. The method differs from the original algorithm by taking into account only a neighborhood of limited length and not all the data in the chain for computations. It then becomes possible to propose a bootstrap subsampling strategy for the computation of forward and backward probabilities, which greatly reduces computation time and memory saving required for EM-based parameter estimation. Comparative experiments regarding the neighborhood size and the bootstrap sample size are conducted by mean of unsupervised classification error rates. Practical interest of such an algorithm is then illustrated through the segmentation of large-size images; classification results confirm the validity and the accuracy of the proposed algorithm while greatly reducing computation and memory requirements.