Local Position Classification for Pattern Discovery in Multivariate Sequential Data

Abstract

Traditional sequential data analysis largely depends on the magnitude of the data with the geometric features of individual data points sometimes being regarded as noise to such analysis. To explore whether these geometric features alone carry some useful information for a better understanding of hidden facts contained in the sequential data, a new method… (More)

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Cite this paper

@inproceedings{Guo2012LocalPC, title={Local Position Classification for Pattern Discovery in Multivariate Sequential Data}, author={William W. Guo}, year={2012} }