Restricted Connection Orthogonal Matching Pursuit For Sparse Subspace Clustering
This work addresses the trade-off between speed and accuracy in subspace clustering, which is important for applications like image and signal processing, though it appears incremental as it builds on existing methods like OMP and SSC.
The paper tackles the computational burden and accuracy loss in Sparse Subspace Clustering by proposing RCOMP-SSC, a noise-robust algorithm that restricts connections during Orthogonal Matching Pursuit iterations, achieving improved clustering accuracy and maintaining low computational time as demonstrated on synthetic data and real-world databases like EYaleB and Usps.
Sparse Subspace Clustering (SSC) is one of the most popular methods for clustering data points into their underlying subspaces. However, SSC may suffer from heavy computational burden. Orthogonal Matching Pursuit applied on SSC accelerates the computation but the trade-off is the loss of clustering accuracy. In this paper, we propose a noise-robust algorithm, Restricted Connection Orthogonal Matching Pursuit for Sparse Subspace Clustering (RCOMP-SSC), to improve the clustering accuracy and maintain the low computational time by restricting the number of connections of each data point during the iteration of OMP. Also, we develop a framework of control matrix to realize RCOMP-SCC. And the framework is scalable for other data point selection strategies. Our analysis and experiments on synthetic data and two real-world databases (EYaleB & Usps) demonstrate the superiority of our algorithm compared with other clustering methods in terms of accuracy and computational time.