Mixtures of skew-t factor analyzers
TLDR
Mixtures of skew-t factor analyzers are very well-suited for model-based clustering of high-dimensional data, giving superior clustering results when compared to a well-established family of Gaussian mixture models.About:
This article is published in Computational Statistics & Data Analysis.The article was published on 2014-09-01 and is currently open access. It has received 112 citations till now. The article focuses on the topics: Mixture model & Determining the number of clusters in a data set.read more
Citations
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Model-Based Clustering
TL;DR: A review of work to date in model-based clustering, from the famous paper by Wolfe in 1965 to work that is currently available only in preprint form, and a look ahead to the next decade or so.
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A mixture of generalized hyperbolic distributions
TL;DR: The authors introduce a mixture of generalized hyperbolic distributions as an alternative to the ubiquitous mixture of Gaussian distributions as well as their near relatives within which the mixture of multivariate t-distributions and the mixtures of skew-t distributions predominate.
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Finite mixtures of canonical fundamental skew $$t$$t-distributions
TL;DR: Lee and McLachlan as mentioned in this paper introduced a finite mixture of canonical fundamental skew $$t$$t (CFUST) distributions for a model-based approach to clustering where the clusters are asymmetric and possibly long-tailed.
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Extending mixtures of factor models using the restricted multivariate skew-normal distribution
TL;DR: The proposed MSNFA model provides an approach to model-based density estimation and clustering of high-dimensional data exhibiting asymmetric characteristics and a computationally feasible Expectation Conditional Maximization (ECM) algorithm is developed for computing the maximum likelihood estimates of model parameters.
References
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Maximum likelihood from incomplete data via the EM algorithm
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Estimating the Dimension of a Model
TL;DR: In this paper, the problem of selecting one of a number of models of different dimensions is treated by finding its Bayes solution, and evaluating the leading terms of its asymptotic expansion.
Estimating the dimension of a model
TL;DR: In this paper, the problem of selecting one of a number of models of different dimensions is treated by finding its Bayes solution, and evaluating the leading terms of its asymptotic expansion.
Journal ArticleDOI
Molecular classification of cancer: class discovery and class prediction by gene expression monitoring.
Todd R. Golub,Todd R. Golub,Donna K. Slonim,Pablo Tamayo,Christine Huard,Michelle Gaasenbeek,Jill P. Mesirov,Hilary A. Coller,Mignon L. Loh,James R. Downing,Michael A. Caligiuri,Clara D. Bloomfield,Eric S. Lander +12 more
TL;DR: A generic approach to cancer classification based on gene expression monitoring by DNA microarrays is described and applied to human acute leukemias as a test case and suggests a general strategy for discovering and predicting cancer classes for other types of cancer, independent of previous biological knowledge.