Stochastic Sparse Subspace Clustering

CVPR 2020 Ying ChenChun-Guang LiChong You

State-of-the-art subspace clustering methods are based on self-expressive model, which represents each data point as a linear combination of other data points. By enforcing such representation to be sparse, sparse subspace clustering is guaranteed to produce a subspace-preserving data affinity where two points are connected only if they are from the same subspace... (read more)

PDF Abstract CVPR 2020 PDF CVPR 2020 Abstract


No code implementations yet. Submit your code now


Results from the Paper

  Submit results from this paper to get state-of-the-art GitHub badges and help the community compare results to other papers.

Methods used in the Paper