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HDclassif: an R Package for Model-Based Clustering and Discriminant Analysis of High-Dimensional Data

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Academic year: 2021

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Table 1: Properties of the HD models and some classical Gaussian models: K is the number of components, d and d k are the intrinsic dimensions of the classes, p is the dimension of the observation space, ρ = Kp + K − 1 is the number of parameters required
Table 2: Models with class specific orientation matrix.
Figure 1: This graph summarizes the results of the estimation of the learning sample of the wine dataset
Figure 2: Selection of the intrinsic dimension of the classes in HDDA using the BIC criterion for the wine dataset.
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