This function summarizes 'fem' objects. It in particular indicates which DLM model has been chosen and displays the loading matrix 'U' if the original dimension is smaller than 10.
● Data Source:
CranContrib
● Keywords:
● Alias: summary.fem
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3 images
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The sparse Fisher-EM algorithm is a sparse version of the Fisher-EM algorithm. The sparsity is introduced within the F step which estimates the discriminative subspace. The sparsity on U is obtained by adding a l1 penalty to the optimization problem of the F step.
● Data Source:
CranContrib
● Keywords:
● Alias: sfem
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1 images
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This function plots different information about 'fem' objects such as model selection, log-likelihood evolution and visualization of the clustered data into the discriminative subspace fitted by the Fisher-EM algorithm.
● Data Source:
CranContrib
● Keywords:
● Alias: plot.fem
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3 images
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The function computes the adjusted Rand index (ARI) which allows to compare two clustering partitions.
● Data Source:
CranContrib
● Keywords:
● Alias: fem.ari
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3 images
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The Fisher-EM algorithm is a subspace clustering method for high-dimensional data. It is based on the Gaussian Mixture Model and on the idea that the data lives in a common and low dimensional subspace. An EM-like algorithm estimates both the discriminative subspace and the parameters of the mixture model.
● Data Source:
CranContrib
● Keywords:
● Alias: fem
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3 images
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The FisherEM package provides an efficient algorithm for the unsupervised classification of high-dimensional data. This FisherEM algorithm models and clusters the data in a discriminative and low-dimensional latent subspace. It also provides a low-dimensional representation of the clustered data.
● Data Source:
CranContrib
● Keywords:
● Alias: FisherEM, FisherEM-package
●
0 images
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