Pattern recognition SIMCA. prediction

Often the goal of a data analysis problem requites more than simple classification of samples into known categories. It is very often desirable to have a means to detect oudiers and to derive an estimate of the level of confidence in a classification result. These ate things that go beyond sttictiy nonparametric pattern recognition procedures. Also of interest is the abiUty to empirically model each category so that it is possible to make quantitative correlations and predictions with external continuous properties. As a result, a modeling and classification method called SIMCA has been developed to provide these capabihties (29—31). 

Supervised pattern recognition methods are used for predicting the class of unkno-wm samples given a training set of samples with known class member-sliip. Tvksmethods are discussed in Section 4.3, KNN and SIMCA,... 

Habits 5 and 6 are not described because POV is not used in this section as a predictive tool. The super ised pattern-recognition technique, SIMCA, uses PCA for class prediction and the details of Habits 5 and 6 for SIMCA are presented in Section 4.3.2.1. 

SIMCA is a supervised pattern recognition technique, which needs to have the data classrhed manually or done using HCA. SIMCA then performs PCA on each class with a sufficient number of factors retained to account for most of the variation within classes. The number of factors retained is very important. If too few are selected, the information in the model set can become distorted. By using a procedure called cross validation, segments of the data are omitted during PCA, and the omitted data are predicted and compared to the actual value. This is repeated for every data element until each point has been excluded once from the determination. The PCA model that yields the minimum prediction error for the omitted data is retained. 

Pattern recognition (techniques such as SIMCA) performed on the acoustic emission spectrum has been used to detect deviations from normal operation and to predict endpoint conditions such as drying endpoint. 

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