  In this paper, we present a novel low rank representation (LRR) algorithm for
data lying on the manifold of square root densities. Unlike traditional LRR
methods which rely on the assumption that the data points are vectors in the
Euclidean space, our new algorithm is designed to incorporate the intrinsic
geometric structure and geodesic distance of the manifold. Experiments on
several computer vision datasets showcase its noise robustness and superior
performance on classification and subspace clustering compared to other
state-of-the-art approaches.
