A randomized approach to large-scale subspace clustering
Panagiotis A. Traganitis, Georgios B. Giannakis · 2016
Subspace clustering has become a popular tool for clustering high-dimensional non-linearly separable data. However, state-of-the-art subspace clustering algorithms do not scale well as the number of data increases. The present paper puts forth a novel randomized subspace clustering scheme for high-volume data based on random projections. Performance of the proposed method is assessed via numerical tests, and is compared with state-of-the-art subspace clustering and large-scale subspace clustering methods.