CUR Decompositions, Similarity Matrices, and Subspace Clustering

11/11/2017
by   Akram Aldroubi, et al.
0

A general framework for solving the subspace clustering problem using the CUR decomposition is presented. The CUR decomposition provides a natural way to construct similarity matrices for data that come from a union of unknown subspaces U=i=1MS_i. The similarity matrices thus constructed give the exact clustering in the noise-free case. A simple adaptation of the technique also allows clustering of noisy data. Two known methods for subspace clustering can be derived from the CUR technique. Experiments on synthetic and real data are presented to test the method.

READ FULL TEXT

Please sign up or login with your details

Forgot password? Click here to reset