Fast Non-Negative Orthogonal Matching Pursuit
نویسندگان
چکیده
منابع مشابه
A fast orthogonal matching pursuit algorithm
The problem of optimal approximation of members of a vector space by a linear combination of members of a large overcomplete library of vectors is of importance in many areas including image and video coding, image analysis, control theory, and statistics. Finding the optimal solution in the general case is mathematically intractable. Matching pursuit, and its orthogonal version, provide greedy...
متن کاملTuning Free Orthogonal Matching Pursuit
Orthogonal matching pursuit (OMP) is a widely used compressive sensing (CS) algorithm for recovering sparse signals in noisy linear regression models. The performance of OMP depends on its stopping criteria (SC). SC for OMP discussed in literature typically assumes knowledge of either the sparsity of the signal to be estimated k0 or noise variance σ , both of which are unavailable in many pract...
متن کاملOrthogonal Matching Pursuit with Replacement
In this paper, we consider the problem of compressed sensing where the goal is to recover all sparsevectors using a small number of fixed linear measurements. For this problem, we propose a novelpartial hard-thresholding operator that leads to a general family of iterative algorithms. While oneextreme of the family yields well known hard thresholding algorithms like ITI and HTP[17, ...
متن کاملOrthogonal Matching Pursuit for Sparse Signal Recovery
We consider the orthogonal matching pursuit (OMP) algorithm for the recovery of a high-dimensional sparse signal based on a small number of noisy linear measurements. OMP is an iterative greedy algorithm that selects at each step the column which is most correlated with the current residuals. In this paper, we present a fully data driven OMP algorithm with explicit stopping rules. It is shown t...
متن کاملGroup Orthogonal Matching Pursuit for Logistic Regression
We consider a matching pursuit approach for variable selection and estimation in logistic regression models. Specifically, we propose Logistic Group Orthogonal Matching Pursuit (LogitGOMP), which extends the Group-OMP procedure originally proposed for linear regression models, to select groups of variables in logistic regression models, given a predefined grouping structure within the explanato...
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 Signal Processing Letters
سال: 2015
ISSN: 1070-9908,1558-2361
DOI: 10.1109/lsp.2015.2393637