Statistical Methods in Pattern Recognition
When measurements group together and begin to form clusters in some measurement feature space, one tends to remark that a pattern is developing. Furthermore, the size and shape of the pattern can be provided a statistical description. In a variety of applications, one is faced with the problem of using these statistical descriptions to classify a particular measurement to a specific cluster; that is, to make a decision regarding which pattern group generated the measurement.
This paper presents an overview of the mathematical considerations that statistical pattern recognition entails. Topics that receive emphasis include normalizations based upon covariance matrix eigen-factorizations, eigen-expansion feature extraction methods, linear classifier functions, and distance measurements. Particular emphasis is given to Linear algebraic techniques that lead to simple computational implementations. Lastly, estimation in a noisy environment is briefly discussed.
KeywordsCovariance Matrix Measurement Vector Linear Classifier Statistical Pattern Recognition Discriminant Vector
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