![]() A simulated example provides an intuitive illustration of their application. In some cases, they are more efficient than the conventional PCA tools. These tools are simple and fast in computation. ![]() We introduce new tools for the assessment of the PCA model complexity such as the plots of the degrees of freedom developed for the orthogonal and score distances, as well as the Extreme and Distance plots, which present a new look at the features of the training and test (new) data. We discuss several important aspects of the PCA exploration of high dimensional datasets, such as estimation of a proper complexity of PCA model, dependence on the data structure, presence of outliers, etc. In this paper we demonstrate that they can also be useful for the exploratory data analysis. Some of them were originally created for solving authentication and classification tasks. However, in the recent decade, several new tools have been developed. Basic tools for exploration and interpretation of Principal Component Analysis (PCA) results are well-known and thoroughly described in many comprehensive tutorials.
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