SIMCA (Soft Independent Modeling of Class Analogy) is a supervised pattern recognition method used in multivariate data analysis. It builds separate PCA (Principal Component Analysis) models for each class of objects, and then uses these models to classify new objects. An object is assigned to the class whose model it best fits. It is commonly used in chemometrics, process monitoring, and other fields where complex data sets need to be classified.
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