Many computer vision algorithms employ subspace models to represent data. The
Low-rank representation (LRR) has been successfully applied in subspace
clustering for which data are clustered according to their subspace structures...
The possibility of extending LRR on Grassmann manifold is explored in this
paper. Rather than directly embedding Grassmann manifold into a symmetric
matrix space, an extrinsic view is taken by building the self-representation of
LRR over the tangent space of each Grassmannian point. A new algorithm for
solving the proposed Grassmannian LRR model is designed and implemented. Several clustering experiments are conducted on handwritten digits dataset,
dynamic texture video clips and YouTube celebrity face video data. The
experimental results show our method outperforms a number of existing methods.