Based on the fact that the Hankel matrix representing clean seismic data is low rank, low-rank approximation methods have been widely utilized for removing noise from seismic data. A common strategy for real seismic data is to perform the low-rank approximations for small local windows where the...
Xu. "Projected Wirtinger Gradient Descent for Low-Rank Hankel Matrix Completion in Spectral Compressed Sensing". In: arXiv preprint arXiv:1507.03707 (2015).J.-F. Cai, S. Liu, and W. Xu, "Projected Wirtinger Gradient Descent for Low-Rank Hankel Matrix Completion in Spectral Compressed Sensing...
Robust recovery of complex exponential signals from random Gaussian projections via low rank Hankel matrix reconstruction. Appl Comput Harmon Anal, 2016, 41: 470–490 Article MathSciNet Google Scholar Cai J F, Wang T, Wei K. Fast and provable algorithms for spectrally sparse signal reconstruction...
Quasi-Hankel Low-Rank Matrix Completion: a Convex Relaxation The completion of matrices with missing values under the rank constraint is a non-convex optimization problem. A popular convex relaxation is based on minimization of the nuclear norm (sum of singular values) of the matrix. For this re...
We invoke the matrix perspective function—the matrix analog of the perspective function—to characterize explicitly the convex hull of epigraphs of simple matrix convex functions under low-rank constraints. Further, we combine the matrix perspective function with orthogonal projection matrices—the matrix...
Structured low-rank approximation is the problem of minimizing a weighted Frobenius distance to a given matrix among all matrices of fixed rank in a linear... G Ottaviani,PJ Spaenlehauer,B Sturmfels - 《Siam Journal on Matrix Analysis & Applications》 被引量: 66发表: 2014年 Software for wei...
If, in addition to being low-rank, the data matrix is Hankel structured, then the fitting model, in addition to being linear, is time-invariant dynamic. In the special case of unstructured low-rank approximation the model is static. A commonly used method to achieve a low-rank approximation...
Our example on optimal low-rank Hankel approximation/model reduction illustrates that the proposed convex relaxation performs consistently better than nuclear norm regularization as well as balanced truncation. 展开 关键词: Low-rank Approximation Model Reduction System Identification k-support norm Compressed...
Anew scheme is proposed, which exploits two low-rank structuresthat exist in MRSI data, one due to partial-separability and theother due to linear predictability. Denoising is performed byarranging the measured data in appropriate matrix forms (i.e.,Casorati and Hankel) and applying low-rank ...
4.1. Low-rank matrix approximation model The Low-Rank Matrix Approximation (LRMA) is a prominent image and video processing technique. Its primary objective is to extract low-rank structures from deteriorated observational data. Mathematically, the LRMA-based model can be expressed as the subsequent ...