Abstract:
Temporal data are continuously collected in a wide range of domains. The increasing availability of such data has led to significant
developments of time series analysis. Time series classification, as
an essential task in time series analysis, aims to assign a set of temporal sequences to different categories. Among various approaches
for time series classification, the distance metric learning based
ones, such as the virtual sequence metric learning (VSML), have
attracted increased attention due to their remarkable performance.
In VSML, virtual sequences attract samples from different classes
to facilitate time series classification. However, the existing VSML
methods simply employ fixed virtual sequences, which might not be
optimal for the subsequent classification tasks. To address this issue,
in this paper, we propose a novel time series classification method
named Discriminative Virtual Sequence Learning (DVSL). Following the unified framework of sequence metric learning, our DVSL
method jointly learns a set of discriminative virtual sequences that
help separate time series samples in a feature space, and optimizes
the temporal alignment by dynamic time warping. Extensive experiments on 15 UCR time series datasets demonstrate the efficiency
of DVSL, compared with several representative baselines.
Authors: Abhilash Dorle,Fangyu Li, Wenzhan Song, Sheng Li
