Rank-constrained inherent clustering for supervised and unsupervised learning Dr. Yiyuan She Department of Statistics Florida State University ABSTRACT: Modern clustering applications are often faced with challenges from high dimensionality and nonconvex clusters. This paper gives a mathematical formulation of low-rank clustering and proposes an optimization based inherent clustering framework. The resulting method enjoys a nice kernel property to apply to …
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