Multidimensional Shrinkage-Thresholding Operator and Group LASSO Penalties
نویسندگان
چکیده
منابع مشابه
Translation-Invariant Shrinkage/Thresholding of Group Sparse Signals
This paper addresses signal denoising when large-amplitude coefficients form clusters (groups). The L1-norm and other separable sparsity models do not capture the tendency of coefficients to cluster (group sparsity). This work develops an algorithm, called ‘overlapping group shrinkage’ (OGS), based on the minimization of a convex cost function involving a group-sparsity promoting penalty functi...
متن کاملForward-LASSO with Adaptive Shrinkage
Both classical Forward Selection and the more modern Lasso provide computationally feasible methods for performing variable selection in high dimensional regression problems involving many predictors. We note that although the Lasso is the solution to an optimization problem while Forward Selection is purely algorithmic, the two methods turn out to operate in surprisingly similar fashions. Our ...
متن کاملCompressed Sensing Recovery via Nonconvex Shrinkage Penalties
The ` minimization of compressed sensing is often relaxed to `, which yields easy computation using the shrinkage mapping known as soft thresholding, and can be shown to recover the original solution under certain hypotheses. Recent work has derived a general class of shrinkages and associated nonconvex penalties that better approximate the original ` penalty and empirically can recover the ori...
متن کاملNetwork Exploration via the Adaptive Lasso and Scad Penalties.
Graphical models are frequently used to explore networks, such as genetic networks, among a set of variables. This is usually carried out via exploring the sparsity of the precision matrix of the variables under consideration. Penalized likelihood methods are often used in such explorations. Yet, positive-definiteness constraints of precision matrices make the optimization problem challenging. ...
متن کاملFuzzy Logic Based Thresholding for Hyper Shrinkage
Signal denoising is the process of reducing the unwanted noise in order to restore the original signal. Donoho and Johnstone’s denoising algorithm based on wavelet thresholding replace the small coefficients by zero and keep or shrink the coefficients with absolute value above the threshold. So the threshold selection becomes more important in signal denoising. In this paper the threshold selec...
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: 2011
ISSN: 1070-9908,1558-2361
DOI: 10.1109/lsp.2011.2139204