Pattern recognition by means of disjoint principal components models

摘要

Pattern recognition based on modelling each separate class by a separate principal components (PC) model is discussed. These PC models are shown to be able to approximate any continuous variation within a single class. Hence, methods based on PC models will, provided that the data are sufficient, recognize any pattern that exists in a given set of objects. In addition, fitting the objects in each class by a separate PC model will, in a simple way, provide information about such matters as the relevance of single variables, “outliers” among the objects and “distances” between different classes. Application to the classical Iris-data of Fisher is used as an illustration.