This function computes the class prediction of a dataset with respect to the model-based supervised and unsupervised classification methods hdda and hddc.
● Data Source:
CranContrib
● Keywords: clustering, hdda, hddc
● Alias: predict.hdc
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This function computes the class prediction of a dataset with respect to the model-based supervised classification method hdmda.
● Data Source:
CranContrib
● Keywords: ~kwd1, ~kwd2
● Alias: predict.hdmda
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This function generates two datasets according to the model [AkBkQkDk] of the HDDA gaussian mixture model paramatrisation (see ref.).
● Data Source:
CranContrib
● Keywords: gaussian, generation
● Alias: simuldata
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HDDA is a model-based discriminant analysis method assuming each class of the dataset live in a proper Gaussian subspace which is much smaller than the original one, the hdda.learn function calculates the parameters of each subspace in order to predict the class of new observation of this kind.
● Data Source:
CranContrib
● Keywords: classification, hdda, predict
● Alias: hdda
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Disciminant analysis and data clustering methods for high dimensional data, based on the asumption that high-dimensional data live in different subspaces with low dimensionality, proposing a new parametrization of the Gaussian mixture model which combines the ideas of dimension reduction and constraints on the model.
● Data Source:
CranContrib
● Keywords: package
● Alias: HDclassif, HDclassif-package
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This function plots Cattell's scree-test or the BIC selection, using parameters coming from hdda or hddc functions.
● Data Source:
CranContrib
● Keywords: cattell, clustering, hdda, hddc
● Alias: plot.hdc
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3 images
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This demonstration uses a PCA on the first two principal axis of the Crabs dataset -that can be found in the package- to show the clustering process of HDDC. At each step of the clustering, the means and directions are shown by, respectively, points and lines. This function should only be used in demo(hddc).
● Data Source:
CranContrib
● Keywords: demo
● Alias: demo_hddc
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HDDC is a model-based clustering method. It is based on the Gaussian Mixture Model and on the idea that the data lives in subspaces with a lower dimension than the dimension of the original space. It uses the Expectation - Maximisation algorithm to estimate the parameters of the model.
● Data Source:
CranContrib
● Keywords: clustering, hddc
● Alias: hddc
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1 images
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hdmda
(Package: HDclassif) :
Mixture Discriminant Analysis with HD Gaussians
HD-MDA implements mixture discriminant analysis (MDA, Hastie & Tibshirani, 1996) with HD Gaussians instead of full Gaussians. Each class is assumed to be made of several class-specific groups in which the data live in low-dimensional subspaces. From a technical point of view, a clustering is done using hddc in each class.
● Data Source:
CranContrib
● Keywords: high-dimensional data, mixture discriminant analysis
● Alias: hdmda
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1 images
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providing 
.