A semiparametric framework clusters high-dimensional elliptical data with heavy tails via cluster-specific centers, a common unknown radial generator, and a shared sparse precision matrix, with GEM algorithm and high-dimensional consistency guarantees.
Model-Based Clustering
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A deterministic compression method reduces high-dimensional discrete data to low-dimensional continuous representations that are injective, approximately Gaussian, and preserve cluster centroid separation for efficient model-based clustering.
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Semiparametric Elliptical Mixture Clustering for High-Dimensional Data
A semiparametric framework clusters high-dimensional elliptical data with heavy tails via cluster-specific centers, a common unknown radial generator, and a shared sparse precision matrix, with GEM algorithm and high-dimensional consistency guarantees.
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Data compression for fast dimension reduction and clustering of high-dimensional discrete data
A deterministic compression method reduces high-dimensional discrete data to low-dimensional continuous representations that are injective, approximately Gaussian, and preserve cluster centroid separation for efficient model-based clustering.