Chapter 7 SIMCA - Classification by Means of Disjoint Cross Validated Principal Components Models

Publisher Summary The classification method Soft Independent Modelling of Class Analogies (SIMCA) is such a method that each class of samples is described by its own principal component model. Thus, in principle, any degree of data collinearity can be accommodated by the models. The chapter presents with a discussion on the important role played by correlation when assessing similarity, and introduces the properties of principal component modelling of relevance to a classification problem. All basic concepts and steps in the SIMCA approach to supervised modelling are thoroughly explored using chemical data obtained in an environmental study. Definition of distance is central in all classification procedures. Euclidean distance in variable space is the most commonly used for measuring similarity between samples. This measure is presented in two-dimensional space. Principal component modelling plays two different roles in the classification of multivariate data, they are as follows: (1) it is a tool for data reduction to obtain low-dimensional orthogonal representations of the multivariate variable- and object-space in which object and variable relationships can be explored, (2) it is used in the SIMCA method to separate the data into a model and a residual matrix from which a scale can be obtained for later classification of samples. Sometimes SIMCA classification is preceded by an unsupervised principal component modelling of the whole data set. The process of detecting and deleting outliers represents one side of the process termed “polishing of classes.”