1 research outputs found
Neural Time Warping For Multiple Sequence Alignment
Multiple sequences alignment (MSA) is a traditional and challenging task for
time-series analyses. The MSA problem is formulated as a discrete optimization
problem and is typically solved by dynamic programming. However, the
computational complexity increases exponentially with respect to the number of
input sequences. In this paper, we propose neural time warping (NTW) that
relaxes the original MSA to a continuous optimization and obtains the
alignments using a neural network. The solution obtained by NTW is guaranteed
to be a feasible solution for the original discrete optimization problem under
mild conditions. Our experimental results show that NTW successfully aligns a
hundred time-series and significantly outperforms existing methods for solving
the MSA problem. In addition, we show a method for obtaining average
time-series data as one of applications of NTW. Compared to the existing
barycenters, the mean time series data retains the features of the input
time-series data.Comment: 11 pages, 5figures, ICASSP 202