基于概率自适应分段对齐的鲁棒时间序列检索
Robust Time-Series Retrieval Using Probabilistic Adaptive Segmental Alignment
摘要：
传统的成对序列比对是基于时间单调性约束下的两个序列的个体样本的匹配。然而，在许多应用设置中，匹配连续性（片段）而不是单个样本可以带来对噪声或局部非因果扰动的额外鲁棒性。本文提出了一种分段序列比对的方法，联合分段和比对两个序列，推广了传统的每样本比对。为了完成这一任务，我们引入了一个距离度量段之间的平均成对距离的基础上，然后提出了一个修改的对HMM（PHMM），结合建议的距离度量来解决联合分割和对齐任务。我们还提出了一个放松我们的模型，提高了计算效率的通用分段PHMM。我们的研究结果表明，这种新的序列相似性的措施，可以导致提高分类性能，同时具有弹性的噪声，对各种序列检索问题，从EEG到运动序列分类。
英文摘要：
Traditional pairwise sequence alignment is based on matching individual samples from two sequences, under time monotonicity constraints. However, in many application settings matching subsequences (segments) instead of individual samples may bring in additional robustness to noise or local non-causal perturbations. This paper presents an approach to segmental sequence alignment that jointly segments and aligns two sequences, generalizing the traditional per-sample alignment. To accomplish this task, we introduce a distance metric between segments based on average pairwise distances and then present a modified pair-HMM (PHMM) that incorporates the proposed distance metric to solve the joint segmentation and alignment task. We also propose a relaxation to our model that improves the computational efficiency of the generic segmental PHMM. 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 sequence retrieval problems, from EEG to motion sequence classification.
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。
