2015/08/18 by Yifan Fu, Fu, Yifan, Junbin Gao +5
Computer Science · Engineering · #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Face and Expression Recognition #Medical Image Segmentation Techniques #Sparse and Compressive Sensing Techniques #cs.CV
paper · pdf · doi:10.48550/arxiv.1508.04198
arxiv created 2015/08/18 · openalex publication_date 2015/08/18 · arxiv updated 2015/08/19 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.