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.
Journal of Machine Learning Research , year =
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A robust sparse clustering method uses spatial medians and automatic feature exclusion to achieve competitive accuracy and better stability than standard K-means on simulated heavy-tailed high-dimensional data.
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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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Sparse $K$-spatial-median clustering for high-dimensional data
A robust sparse clustering method uses spatial medians and automatic feature exclusion to achieve competitive accuracy and better stability than standard K-means on simulated heavy-tailed high-dimensional data.