SPARSE REGRESSION-BASED MULTIPLE SEQUENCE ALIGNMENT

Aligning two time series can be performed effectively using a widely used technique - dynamic time warping. However, as more and more data are collected, alignment is simultaneously needed for multiple sequences in many applications. Unfortunately, constructing accurate multiple alignment is a computationally intense and complex task because the problem is well known to be NP-hard. In this paper, we develop a relaxed formulation for the multiple alignment problem based on a regression-coding framework. We then propose an efficient algorithm that employs sparse approximation to reduce computational cost for the relaxation. The goodness of our approach is theoretically analyzed and empirically evaluated on both synthetic and real-world datasets.