Kernelized LRR on Grassmann Manifolds for Subspace Clustering

TL;DR

This paper introduces a kernelized low rank representation model on Grassmann manifolds for subspace clustering, demonstrating superior performance in computer vision data analysis tasks.

Contribution

It generalizes LRR from Euclidean spaces to Grassmann manifolds within a kernelized framework, enabling better clustering of subspaces.

Findings

Outperforms state-of-the-art subspace clustering methods

Effective in computer vision applications

Demonstrates robustness on practical datasets

Abstract

Low rank representation (LRR) has recently attracted great interest due to its pleasing efficacy in exploring low-dimensional sub- space structures embedded in data. One of its successful applications is subspace clustering, by which data are clustered according to the subspaces they belong to. In this paper, at a higher level, we intend to cluster subspaces into classes of subspaces. This is naturally described as a clustering problem on Grassmann manifold. The novelty of this paper is to generalize LRR on Euclidean space onto an LRR model on Grassmann manifold in a uniform kernelized LRR framework. The new method has many applications in data analysis in computer vision tasks. The proposed models have been evaluated on a number of practical data analysis applications. The experimental results show that the proposed models outperform a number of state-of-the-art subspace clustering methods.