Learning object samples

Exploratory data analysis has the aim to learn about the data distribution (clusters, groups of similar objects). In multivariate data analysis, an X-matrix (objects/samples characterized by a set of variables/measurements) is considered. Most used method for this purpose is PCA, which uses latent variables with maximum variance of the scores (Chapter 3). Another approach is cluster analysis (Chapter 6). 

The result from cluster analysis presented in Fig. 9-2 is subjected to MVDA (for mathematical fundamentals see Section 5.6 or [AHRENS and LAUTER, 1981]). The principle of MVDA is the separation of predicted classes of objects (sampling points). In simultaneous consideration of all the features observed (heavy metal content), the variance of the discriminant functions is maximized between the classes and minimized within them. The classification of new objects into a priori classes or the reclassification of the learning data set is carried out using the values of the discriminant function. These values represent linear combinations of the optimum separation set of the original features. The result of the reclassification is presented as follows ... 

To help you review these learning objectives, the numbers of related sections ( ), sample problems (SP), and upcoming end-of-chapter problems (EP) are listed in parentheses. 

Learning Objectives are listed, with section, sample problem, and end-of-chapter problem numbers, to focus you on key concepts and skills. 

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