A non-probabilistic recognizer of stochastic signals based on KLT
Signal Processing
A multi-resolution hidden Markov model using class-specific features
IEEE Transactions on Signal Processing
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Probability density functions (pdf's) of high dimensionality are impractical to estimate from real data. For accurate estimation, the dimensionality of the pdf can be at most 5-10. In order to reduce the dimensionality a sufficient statistic is usually employed. When none is available, there is no universal agreement on how to proceed. We show how to construct a high-dimension pdf based on the pdf of a low-dimensional statistic that is closest to the true one in the sense of divergence. The latter criterion asymptotically minimizes the probability of error in a decision rule. An application to feature selection for classification is described