scholar.google.com › citations
PDF | On Oct 19, 2019, Imran Razzak and others published Nuclear Norm Minimization in Frequency Domain for Complex Noise | Find, read and cite all the ...
Experimental results clearly show that the weighted minimization in the frequency domain outperforms many state-of-the-art de-noising algorithms in terms of ...
Experimental results clearly show that the weighted minimization in the frequency domain outperforms many state-of-the-art de-noising algorithms in terms of ...
Aug 26, 2014 · In the presence of noise rank determination becomes difficult and the low rank estimates lose the structure required for exact realizability.
Missing: Complex | Show results with:Complex
Abstract— The nuclear norm is an effective proxy for matrix rank in a range of minimization problems, including subspace identification. Nuclear norm-based ...
Missing: Complex | Show results with:Complex
In this section, we propose a nuclear norm minimization framework for DOA estimation by exploiting the full aperture of the extended observations, which can ...
Abstract—Subspace identification techniques have gained widespread acceptance as a method of obtaining a low-order model from data.
Frequency domain subspace identification is an effective means of obtaining a low-order model from frequency domain data. In the noisy data case using a ...
Missing: Complex | Show results with:Complex
Jan 26, 2017 · Traditional robust principal component analysis (RPCA) assumes that the observed data are corrupted by some sparse noise (e.g., Laplacian noise).
Minimum nuclear norm solutions often have low rank and in certain applications, for example, low-rank matrix completion problems, the quality of the heuristic ...