- Title
- GDTW-P-SVMs: variable-length time series analysis using support vector machines
- Creator
- Jalalian, Arash; Chalup, Stephan K.
- Relation
- ARC.DP1092679
- Relation
- Neurocomputing Vol. 99, Issue 1, p. 270-282
- Publisher Link
- http://dx.doi.org/10.1016/j.neucom.2012.07.006
- Publisher
- Elsevier
- Resource Type
- journal article
- Date
- 2013
- Description
- We describe a new technique for sequential data analysis, called GDTW-P-SVMs. It is a maximum margin method for the construction of classifiers with variable-length input series. It employs potential support vector machines (P-SVMs) and Gaussian Dynamic Time Warping (GDTW) to waive the fixed-length restriction of feature vectors in training and test data. As a result, GDTW-P-SVMs enjoy the P-SVM method's properties such as the ability to: (i) handle data and kernel matrices that are neither positive definite nor square and (ii) minimise a scale-invariant capacity measure. The new technique elaborates on the P-SVM kernel functions, by utilising the well-known dynamic time warping algorithm to provide an elastic distance measure for the kernel functions. Benchmarks for classification are performed with several real-world data sets from the UCR time series classification/clustering page, the GeoLife trajectory data set, and the UCI Machine Learning Repository. The data sets include data with both variable and fixed-length input series. The results show that the new method performs significantly better than the benchmarked standard classification methods.
- Subject
- support vector machines; dynamic time warping; time series classification; sequential data analysis
- Identifier
- http://hdl.handle.net/1959.13/1042985
- Identifier
- uon:14157
- Identifier
- ISSN:0925-2312
- Language
- eng
- Full Text
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