Motion Segmentation Using Collaborative Low-Rank and Sparse Subspace Clustering
Yao Zhang, Min Li · 2017
We propose a method based on the collaborative Low-Rank Representation (LRR) and Sparse Subspace Clustering (SSC) to cluster data drawn from multiple linear subspaces in a high-dimensional space. Given a set of data vectors, Collaborative Low-Rank and Sparse Subspace Clustering(CLRS) want to seek a better representation among the candidates that represent all vectors as affine combination of the bases in a dictionary. Both theoretical and experimental results show that CLRS is a promising method for subspace segmentation.