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A method that indicates the numbers of components to use in fitting the three-mode principal components analysis (3MPCA) model is proposed. This method, called DIFFIT, aims to find an optimal balance between the fit of solutions for the 3MPCA model and the numbers of components. The achievement of DIFFIT is compared with that of two other methods, both(More)
The present study aimed to examine the construct validity of three aspects of attention, namely focused, divided, and supervisory control of attention. Factor-analytic techniques were applied to scores of healthy subjects on a series of neuropsychological tests tapping these aspects of attention. The two components found did not match the hypothesized(More)
MOTIVATION Modern functional genomics generates high-dimensional datasets. It is often convenient to have a single simple number characterizing the relationship between pairs of such high-dimensional datasets in a comprehensive way. Matrix correlations are such numbers and are appealing since they can be interpreted in the same way as Pearson's correlations(More)
Principal Component Analysis (PCA) is a well-known tool often used for the exploratory analysis of a numerical data set. Here an extension of classical PCA is proposed, which deals with fuzzy data (in short PCAF), where the elementary datum cannot be recognized exactly by a speciÿc number but by a center, two spread measures and a membership function.(More)
Three-way component analysis techniques are designed for descriptive analysis of 3-way data, for example, when data are collected on individuals, in different settings, and on different measures. Such techniques summarize all information in a 3-way data set by summarizing, for each way of the 3-way data set, the associated entities through a few components(More)