Abstract
In this paper a scheme for unsupervised probabilistic time series classification is detailed. The technique utilizes autocorrelation terms as discriminatory features and employs the Volterra Connectionist Model (VCM) to transform the multi-dimensional feature information of each training vector to a one-dimensional classification space. This allows the probability density functions (PDFs) of the scalar classification indices to be represened as a function of the classifier weights. The weight values are chosen so as to maximize the separability of the class conditional PDFs. Statistical similarity tests based on the overlap area of the PDFs are then performed to determine the class membership of each training vector. Results are presented that illustrate the performance of the scheme applied to synthetic and real world data.
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