  This article explores subspace clustering algorithms using CUR
decompositions, and examines the effect of various hyperparameters in these
algorithms on clustering performance on two real-world benchmark datasets, the
Hopkins155 motion segmentation dataset and the Yale face dataset. Extensive
experiments are done for a variety of sampling methods and oversampling
parameters for these datasets, and some guidelines for parameter choices are
given for practical applications.
