On Matrix Factorizations in Subspace Clustering
This work offers incremental improvements for researchers and practitioners applying subspace clustering to real-world datasets like motion segmentation and face recognition.
The paper investigates how different hyperparameters in subspace clustering algorithms using CUR decompositions affect clustering performance on the Hopkins155 motion segmentation and Yale face datasets, providing practical guidelines for parameter selection.
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.