The primary goal of this article is to extend the reciprocal LASSO for applications binary and survival outcomes. We consider least squares approximation (LSA) as a solver problem. LSA general theoretical framework that includes generalized linear models, Cox regression, many others special cases. By applying regularization, we transfer original problem into an asymptotically equivalent While e...

In this paper, we introduce a simple method for the Cauchy problem. This new finite element method is based on least squares methodology with discontinuous approximations which can be implemented and analyzed easily. This discontinuous Galerkin finite element method is flexible to work with general unstructured meshes. Error estimates of the finite element solution are derived. The numerical ex...

We present a highly accurate least-squares (LS) alternative to the theoretically optimal maximum likelihood (ML) estimator for homographies between two images. Unlike ML, our estimator is non-iterative and yields a solution even in the presence of large noise. By rigorous error analysis, we derive a “hyperLS” estimator which is unbiased up to second order noise terms. We also introduce a comput...

We present a method for computing partial spectra of Hermitian matrices, based on a combination of subspace iteration with rational filtering. In contrast with classical rational filters derived from Cauchy integrals or from uniform approximations to a step function, we adopt a least-squares (LS) viewpoint for designing filters. One of the goals of the proposed approach is to build a filter tha...

numerical solutions obtained by the meshless local petrov–galerkin (mlpg) method are presented for two-dimensional steady-state heat conduction problems. the mlpg method is a truly meshless approach, and neither the nodal connectivity nor the background mesh is required for solving the initial-boundary-value problem. the penalty method is adopted to efficiently enforce the essential boundary co...

This paper presents a new direct method for estimating the average center of rotation (CoR). An existing least-squares (LS) solution has been shown by previous works to have reduced accuracy for data with small range of motion (RoM). Alternative methods proposed to improve the CoR estimation use iterative algorithms. However, in this paper we show that with a carefully chosen normalization sche...

In this paper we propose three iterative greedy algorithms for compressed sensing, called iterative alternating direction (IAD), normalized iterative alternating direction (NIAD) and alternating direction pursuit (ADP), which stem from the iteration steps of alternating direction method of multiplier (ADMM) for `0-regularized least squares (`0-LS) and can be considered as the alternating direct...

Several computer vision problems lead to linear systems affected by noise. These are commonly solved by least-squares estimators, the most popular being ordinary least squares (LS), total least squares (TLS) and generalized total least squares (GTLS). However, the statistical or structural assumptions of these theoretical estimators are very often violated in practice. Given that their computat...

fuzzy linear regression models are used to obtain an appropriate linear relation between a dependent variable and several independent variables in a fuzzy environment. several methods for evaluating fuzzy coefficients in linear regression models have been proposed. the first attempts at estimating the parameters of a fuzzy regression model used mathematical programming methods. in this the...