Similarity Learning for Time Series Classification

Autor:	Nicolae, Maria-Irina, Gaussier, Éric, Habrard, Amaury, Sebban, Marc
Rok vydání:	2016
Předmět:	Computer Science - Learning
Druh dokumentu:	Working Paper
Popis:	Multivariate time series naturally exist in many fields, like energy, bioinformatics, signal processing, and finance. Most of these applications need to be able to compare these structured data. In this context, dynamic time warping (DTW) is probably the most common comparison measure. However, not much research effort has been put into improving it by learning. In this paper, we propose a novel method for learning similarities based on DTW, in order to improve time series classification. Making use of the uniform stability framework, we provide the first theoretical guarantees in the form of a generalization bound for linear classification. The experimental study shows that the proposed approach is efficient, while yielding sparse classifiers. Comment: Techreport
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/1610.04783 Zobrazit plný text záznamu View this record from Arxiv