Robust Subspace Clustering With Compressed Data

• Published in: IEEE transactions on image processing Vol. 28; no. 10; pp. 5161 - 5170
• Main Authors: Liu, Guangcan, Zhang, Zhao, Liu, Qingshan, Xiong, Hongkai
• Format: Journal Article
• Language: English
• Published: United States IEEE 01-10-2019; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Automation; Clustering; compressive sensing; Dimensionality reduction; Image coding; Information science; low-rankness; Principal component analysis; Robustness; Sensors; Sparse matrices; sparsity; Statistical methods; Subspace clustering; Subspaces
• ISSN: 1057-7149; 1941-0042
• DOI: 10.1109/TIP.2019.2917857

Dimension reduction is widely regarded as an effective way for decreasing the computation, storage, and communication loads of data-driven intelligent systems, leading to a growing demand for statistical methods that allow analysis (e.g., clustering) of compressed data. We therefore study in this paper a novel problem called compressive robust subspace clustering, which is to perform robust subspace clustering with the compressed data, and which is generated by projecting the original high-dimensional data onto a lower-dimensional subspace chosen at random. Given only the compressed data and sensing matrix, the proposed method, row space pursuit (RSP), recovers the authentic row space that gives correct clustering results under certain conditions. Extensive experiments show that RSP is distinctly better than the competing methods, in terms of both clustering accuracy and computational efficiency.