Convergence and Stability of Iteratively Re-weighted Least Squares Algorithms
نویسندگان
چکیده
منابع مشابه
Convergence and Stability of Iteratively Re-weighted Least Squares Algorithms for Sparse Signal Recovery in the Presence of Noise
In this paper, we study the theoretical properties of a class of iteratively re-weighted least squares (IRLS) algorithms for sparse signal recovery in the presence of noise. We demonstrate a one-toone correspondence between this class of algorithms and a class of Expectation-Maximization (EM) algorithms for constrained maximum likelihood estimation under a Gaussian scale mixture (GSM) distribut...
متن کاملConjugate gradient acceleration of iteratively re-weighted least squares methods
Iteratively Re-weighted Least Squares (IRLS) is a method for solving minimization problems involving non-quadratic cost functions, perhaps non-convex and non-smooth, which however can be described as the infimum over a family of quadratic functions. This transformation suggests an algorithmic scheme that solves a sequence of quadratic problems to be tackled efficiently by tools of numerical lin...
متن کاملIteratively re-weighted least-squares and PEF-based interpolation
Interpolation methods frequently deal poorly with noise. Least-squares based interpolation methods can deal well with noise, as long as it is Gaussian and zero-mean. When this is not the case, other methods are needed. I use an iteratively-reweighted least-squares scheme to interpolate both regular and sparse data with non-stationary prediction-error filters. I show that multi-scale methods are...
متن کاملConvergence and Stability of a Class of Iteratively Re-weighted Least Squares Algorithms for Sparse Signal Recovery in the Presence of Noise.
In this paper, we study the theoretical properties of a class of iteratively re-weighted least squares (IRLS) algorithms for sparse signal recovery in the presence of noise. We demonstrate a one-to-one correspondence between this class of algorithms and a class of Expectation-Maximization (EM) algorithms for constrained maximum likelihood estimation under a Gaussian scale mixture (GSM) distribu...
متن کاملRobust Data Whitening as an Iteratively Re-weighted Least Squares Problem
The entries of high-dimensional measurements, such as image or feature descriptors, are often correlated, which leads to a bias in similarity estimation. To remove the correlation, a linear transformation, called whitening, is commonly used. In this work, we analyze robust estimation of the whitening transformation in the presence of outliers. Inspired by the Iteratively Re-weighted Least Squar...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: IEEE Transactions on Signal Processing
سال: 2014
ISSN: 1053-587X,1941-0476
DOI: 10.1109/tsp.2013.2287685