Riemannian Manifolds clustering via Geometric median
In this paper, we propose a new kernel function that makes use of Riemannian geodesic distance s 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 on Riemannian manifolds. In addition to improving the clustering results, Using both Riemannian Manifolds and Euclidean spaces, We compare the geometric median shift and mean shift algorithms on synthetic and real data sets for the tasks of clustering.
Data provided are for informational purposes only. Although carefully collected, accuracy cannot be guaranteed. The impact factor represents a rough estimation of the journal's impact factor and does not reflect the actual current impact factor. Publisher conditions are provided by RoMEO. Differing provisions from the publisher's actual policy or licence agreement may be applicable.