Near-Oracle Performance of Greedy Block-Sparse Estimation Techniques from Noisy Measurements

This paper examines the ability of greedy algorithms to estimate a block sparse parameter vector from noisy measurements. In particular, block sparse versions of the orthogonal matching pursuit and thresholding algorithms are analyzed under both adversarial and Gaussian noise models. In the adversarial setting, it is shown that estimation accuracy comes within a constant factor of the noise power. Under Gaussian noise, the Cram

• Type: Journal
• Year: 2010
• Where: CORR
• Authors: Zvika Ben-Haim, Yonina C. Eldar
• Keywords: CORR 2010, Education, Gaussian Noise Models, Orthogonal Matching Pursuit, Sparse