A new perspective on centroid algorithms for unsupervised discrete clustering in Riemannian manifolds: an approach via optimization
This work presents a new perspective on a specific class of partition-based algorithms for unsupervised discrete clustering in complete finite-dimensional Riemannian manifolds by introducing statistical shape analyses to classify clusters and improve label selection.
Ronaldo Malheiros Gregorio, Charlan Dellon da Silva Alves, Sérgio Assunção Monteiro et al.
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