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)

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