论文标题
多个序列对齐的神经时间扭曲
Neural Time Warping For Multiple Sequence Alignment
论文作者
论文摘要
多序列对齐（MSA）是时间序列分析的传统且具有挑战性的任务。 MSA问题被称为离散优化问题，通常通过动态编程来解决。但是，计算复杂性相对于输入序列的数量呈指数增长。在本文中，我们提出了神经时间扭曲（NTW），将原始MSA放松以进行连续优化，并使用神经网络获得对齐。通过NTW获得的解决方案保证是在轻度条件下原始离散优化问题的可行解决方案。我们的实验结果表明，NTW成功地对齐了一百个时间序列，并显着优于解决MSA问题的现有方法。此外，我们还展示了一种获得平均时间序列数据作为NTW的应用之一的方法。与现有的重中心相比，平均时间序列数据保留了输入时间序列数据的功能。
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.
