Informatics and Applications
2016, Volume 10, Issue 2, pp 48-57
METRIC LEARNING IN MULTICLASS TIME SERIES CLASSIFICATION PROBLEM
- R. V. Isachenko
- V. V. Strijov
Abstract
This paper is devoted to the problem of multiclass time series classification. It is proposed to align time series in relation to class centroids. Building of centroids and alignment of time series is carried out by the dynamic time warping algorithm. The accuracy of classification depends significantly on the metric used to compute distances between time series. The distance metric learning approach is used to improve classification accuracy.
The metric learning procedure modifies distances between objects to make objects from the same cluster closer and from the different clusters more distant. The distance between time series is measured by the Mahalanobis metric.
The distance metric learning procedure finds the optimal transformation matrix for the Mahalanobis metric. To calculate quality of classification, a computational experiment on synthetic data and real data of human activity recognition was carried out.
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[+] About this article
Title
METRIC LEARNING IN MULTICLASS TIME SERIES CLASSIFICATION PROBLEM
Journal
Informatics and Applications
2016, Volume 10, Issue 2, pp 48-57
Cover Date
2016-05-30
DOI
10.14357/19922264160205
Print ISSN
1992-2264
Publisher
Institute of Informatics Problems, Russian Academy of Sciences
Additional Links
Key words
time series classification; time series alignment; distance metric learning; LMNN algorithm
Authors
R. V. Isachenko  and V. V. Strijov 
Author Affiliations
Moscow Institute of Physics and Technology, 9 Institutskiy Per., Dolgoprudny, Moscow Region 141700, Russian
Federation
A. A. Dorodnicyn Computing Centre, Federal Research Center "Computer Science and Control" of the Russian
Academy of Sciences, 40 Vavilov Str., Moscow 119333, Russian Federation
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