Geometric Median-Shift over Riemannian Manifolds

Yang Wang, Xiaodi Huang

Research output: Book chapter/Published conference paperConference paperpeer-review

3 Citations (Scopus)


Manifold clustering finds wide applications in many areas. In this paper, we propose a new kernel function that makes use of Riemannian geodesic distances among data points, and present a Geometric median shift algorithm over Riemannian Manifolds. Relying on the geometric median shift, together with geodesic distances, our approach is able to effectively cluster data points distributed over Riemannian manifolds. In addition to improving the clustering results, the complexity for calculating geometric median is reduced to O(n 2), compared to O(n 2logn 2) for Tukey median. Using both Riemannian Manifolds and Euclidean spaces, we compare the geometric median shift and mean shift algorithms for clustering synthetic and real data sets.
Original languageEnglish
Title of host publicationPRICAI 2010
Subtitle of host publicationTrends in Artificial Intelligence.
EditorsByoung-Tak Zhang, Mehmet A Orgun
Place of PublicationGermany
Number of pages12
Publication statusPublished - 2010
EventPacific Rim International Conference on Artificial Intelligence - Daegu, Korea, Korea, Republic of
Duration: 30 Aug 201002 Sep 2010


ConferencePacific Rim International Conference on Artificial Intelligence
Country/TerritoryKorea, Republic of


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