8Mixture models for high-dimensional data
8.1 Challenges of high-dimensional mixture models
Clustering in high-dimensional spaces is a recurrent problem in many domains, for example, in genotype analysis, object recognition, and so on, especially for model-based methods. Due to the famous “curse of dimensionality,” model-based clustering methods are often over-parameterized with unsatisfactory performance for high-dimensional data.
Since high-dimensional data usually lives in low-dimensional subspaces hidden in the original space, many efforts have been made to allow model-based methods to efficiently cluster high-dimensional data.
8.2 Mixtures of factor analyzers
To reduce the dimension of multivariate data, ...
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