This contribution focuses, within the `1-Potts model, on the automated estimation of the regularization parameter balancing the `1 data fidelity term and the TV`0 penalization. Variational approaches based on total variation gained considerable interest to solve piecewise constant denoising problems thanks to their deterministic setting and low computational cost. However, the quality of the ac...

This paper presents a comparative study of five parameter estimation algorithms on four NLP tasks. Three of the five algorithms are well-known in the computational linguistics community: Maximum Entropy (ME) estimation with L2 regularization, the Averaged Perceptron (AP), and Boosting. We also investigate ME estimation with L1 regularization using a novel optimization algorithm, and BLasso, whi...

Gravity data inversion is one of the important steps in the interpretation of practical gravity data. The inversion result can be obtained by minimization of the Tikhonov objective function. The determination of an optimal regularization parameter is highly important in the gravity data inversion. In this work, an attempt was made to use the active constrain balancing (ACB) method to select the...

It has been shown that space-variant regularization in image restoration provides better results than space-invariant regularization. However, the optimal choice of the regularization parameter is usually unknown a priori. In previous work, the generalized cross-validation (GCV) criterion was shown to provide accurate estimates of the optimal regularization parameter. The author introduces a mo...

A wavelet regularization approach is presented for dealing with an ill-posed problem of adjoint parameter estimation applied to estimating inflow parameters from down-flow data

This project surveys and examines optimization approaches proposed for parameter estimation in Least Squares linear regression models with an L1 penalty on the regression coefficients. We first review linear regression and regularization, and both motivate and formalize this problem. We then give a detailed analysis of 8 of the varied approaches that have been proposed for optimizing this objec...

Hyperspectral image (HSI) super-resolution is commonly used to overcome the hardware limitations of existing hyperspectral imaging systems on spatial resolution. It fuses a low-resolution (LR) HSI and high-resolution (HR) conventional same scene obtain an HR HSI. In this work, we propose method that integrates physical model deep prior information. Specifically, novel, yet effective two-stream ...

Regularization Algorithm (also called Regularization Network) is a technique for solving problems of learning from examples – in particular, the problem of approximating a multivariate function from sparse data. We analyze behavior of Regularization Algorithm for regularizator parameter equal to zero. We propose an approximative version of algorithm in order to overcome the computational cost f...