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Found 9 papers, 1 papers with code
Multichannel sleep spindle detection using sparse low-rank optimization
Using a non-linear signal model, which assumes the input EEG to be the sum of a transient and an oscillatory component, we propose a multichannel transient separation algorithm.
Ranked #4 on
Spindle Detection
on MASS SS2
Improved Sparse Low-Rank Matrix Estimation
Further, we show how to set the parameters of the non-convex penalty functions, in order to ensure that the objective function is strictly convex.
Enhanced Low-Rank Matrix Approximation
This letter proposes to estimate low-rank matrices by formulating a convex optimization problem with non-convex regularization.
Sparsity-based Correction of Exponential Artifacts
This paper describes an exponential transient excision algorithm (ETEA).
Convex Denoising using Non-Convex Tight Frame Regularization
To more accurately estimate non-zero values, we propose the use of a non-convex regularizer, chosen so as to ensure convexity of the objective function.
Image Restoration using Total Variation with Overlapping Group Sparsity
Usually, the high-order total variation (HTV) regularizer is an good option except its over-smoothing property.
Group-Sparse Signal Denoising: Non-Convex Regularization, Convex Optimization
Convex optimization with sparsity-promoting convex regularization is a standard approach for estimating sparse signals in noise.
Sparse Signal Estimation by Maximally Sparse Convex Optimization
For this purpose, this paper describes the design and use of non-convex penalty functions (regularizers) constrained so as to ensure the convexity of the total cost function, F, to be minimized.
