基于全局对齐的时间序列核
A kernel for time series based on global alignments
摘要：
在本文中，我们提出了一个新的家庭的内核处理时间序列，特别是语音数据，内核的方法，其中包括流行的算法，如支持向量机的框架内。这些内核通过考虑相同的一组基本操作（即令牌的替换和重复）来详细说明众所周知的动态时间规整（DTW）距离家族，以将一个序列映射到另一个序列上。DTW算法将这些操作中的每一个与给定的分数相关联，使用动态规划技术来计算具有高总分的最佳操作序列。在本文中，我们认为，而不是所有可能的路线跨越的分数，采取平滑的版本，其最大值，并推导出这个公式的内核。我们证明，这个内核是正定的有利条件下，并显示它可以有效地调整为实际应用中，我们报告令人鼓舞的结果上的语音识别任务。
英文摘要：
We propose in this paper a new family of kernels to handle times series, notably speech data, within the framework of kernel methods which includes popular algorithms such as the Support Vector Machine. These kernels elaborate on the well known Dynamic Time Warping (DTW) family of distances by considering the same set of elementary operations, namely substitutions and repetitions of tokens, to map a sequence onto another. Associating to each of these operations a given score, DTW algorithms use dynamic programming techniques to compute an optimal sequence of operations with high overall score. In this paper we consider instead the score spanned by all possible alignments, take a smoothed version of their maximum and derive a kernel out of this formulation. We prove that this kernel is positive definite under favorable conditions and show how it can be tuned effectively for practical applications as we report encouraging results on a speech recognition task.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
