Diffusion K-means clustering on manifolds: Provable exact recovery via semidefinite relaxations

Research output: Contribution to journalArticle

Abstract

We introduce the diffusion K-means clustering method on Riemannian submanifolds, which maximizes the within-cluster connectedness based on the diffusion distance. The diffusion K-means constructs a random walk on the similarity graph with vertices as data points randomly sampled on the manifolds and edges as similarities given by a kernel that captures the local geometry of manifolds. The diffusion K-means is a multi-scale clustering tool that is suitable for data with non-linear and non-Euclidean geometric features in mixed dimensions. Given the number of clusters, we propose a polynomial-time convex relaxation algorithm via the semidefinite programming (SDP) to solve the diffusion K-means. In addition, we also propose a nuclear norm regularized SDP that is adaptive to the number of clusters. In both cases, we show that exact recovery of the SDPs for diffusion K-means can be achieved under suitable between-cluster separability and within-cluster connectedness of the submanifolds, which together quantify the hardness of the manifold clustering problem. We further propose the localized diffusion K-means by using the local adaptive bandwidth estimated from the nearest neighbors. We show that exact recovery of the localized diffusion K-means is fully adaptive to the local probability density and geometric structures of the underlying submanifolds.

Original languageEnglish (US)
JournalApplied and Computational Harmonic Analysis
DOIs
StateAccepted/In press - Jan 1 2020

Keywords

  • Adaptivity
  • Diffusion distance
  • K-means
  • Laplace-Beltrami operator
  • Manifold clustering
  • Mixing times
  • Random walk on random graphs
  • Riemannian submanifolds
  • Semidefinite programming

ASJC Scopus subject areas

  • Applied Mathematics

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