Improved sequence classification using adaptive segmental sequence alignment

Traditional pairwise sequence alignment is based on matching individual samples from two sequences, under time monotonicity constraints. However, in some instances matching two segments of points may be preferred and can result in increased noise robustness. This paper presents an approach to segmental sequence alignment based on adaptive pairwise segmentation. We introduce a distance metric between segments based on average pairwise distances, which addresses deficiencies of prior approaches. We then present a modified pair-HMM that incorporates the proposed distance metric and use it to devise an efficient algorithm to jointly segment and align the two sequences. Our results demonstrate that this new measure of sequence similarity can lead to improved classification performance, while being resilient to noise, on a variety of problems, from EEG to motion sequence classification.