Probabilistic Feature Extraction from Multivariate Time Series Using Spatio-Temporal Constraints

Abstract

A novel nonlinear probabilistic feature extraction method, called Spatio-Temporal Gaussian Process Latent Variable Model, is introduced to discover generalised and continuous low dimensional representation of multivariate time series data in the presence of stylistic variations. This is achieved by incorporating a new spatio-temporal constraining prior over… (More)
DOI: 10.1007/978-3-642-20847-8_15

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Cite this paper

@inproceedings{Lewandowski2011ProbabilisticFE, title={Probabilistic Feature Extraction from Multivariate Time Series Using Spatio-Temporal Constraints}, author={Michal Lewandowski and Dimitrios Makris and Jean-Christophe Nebel}, booktitle={PAKDD}, year={2011} }