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As the result, a set of relevant features, f, with minimum redundancy and maximum class discriminability is obtained.
In this article, we used feature selection methods we previously developed [33, 48, 49] to select the optimal feature subset with minimum redundancy and maximum class discriminability.
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Here we measured class discriminability, using Fisher Linear Discriminant Analysis and Non-Parametric-Discriminant-Analysis, and computing Mutual Information as a function of image size (and thus spatial frequency).
Therefore, in [22], a number of discriminative ETGs are selected according to their class discriminability, where for the eigentensor is defined as (8).
The school has two classrooms per grade, and the maximum class size is 18.
Maximum class size is 12 people.
The results highlight that combining the two datasets increases instrument mislabeling, hence significantly reducing class discriminability which is indicative of higher heterogeneity across the two datasets (Fig. 5).
This rise in demand is also likely to raise questions about the maximum class size limits for infant classes.
Thus, to perform this study we evaluated three class discriminability measures as a function of the spatial frequency content of face images to find out if there is a maximum in the same representation found with the psychophysical studies.
In this context, the class discriminability of the projected data is estimated from the data scatter in the projected space.
Classic discriminant analysis techniques were often applied to linear feature extraction in order to find the projection matrix that preserves the class discriminability of data points.
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