Speckle noise removal using a two-step weighted robust regression
Sign Up to like & getrecommendations! Published in 2019 at "Optics Communications"
DOI: 10.1016/j.optcom.2019.07.027
Abstract: Abstract The speckle usually degrades the signal quality for coherent detection or imaging. In this paper, under the single-image constraint, we propose a two-step weighted robust regression method for speckle removal, where the first step… read more here.
Keywords: robust regression; removal; weighted robust; step ... See more keywords
Is Alaska an Aberration or the Worst Case? A Robust Regression Approach to Understanding Inter-State Variations of Forcible Rape
Sign Up to like & getrecommendations! Published in 2017 at "Deviant Behavior"
DOI: 10.1080/01639625.2016.1257883
Abstract: ABSTRACT Alaska’s rape rate perennially exceeds that of all other states. This study considers Alaska’s socioeconomic extremes—its frontier demography, its externally dependent resource economy, and its military presence—as explanations for inter-state variations of sexual violence.… read more here.
Keywords: state; alaska aberration; rape; state variations ... See more keywords
Robust-regression-type estimators for improving mean estimation of sensitive variables by using auxiliary information
Sign Up to like & getrecommendations! Published in 2019 at "Communications in Statistics - Theory and Methods"
DOI: 10.1080/03610926.2019.1645857
Abstract: Abstract In case of sensitive research, estimation of mean is a major concern in survey studies and regression estimators utilizing traditional regression coefficient are the most favored choices for it. Recently, Zaman and Bulut [2018.… read more here.
Keywords: robust regression; type estimators; mean estimation; regression ... See more keywords
Generalized Robust Regression for Jointly Sparse Subspace Learning
Sign Up to like & getrecommendations! Published in 2019 at "IEEE Transactions on Circuits and Systems for Video Technology"
DOI: 10.1109/tcsvt.2018.2812802
Abstract: Ridge regression is widely used in multiple variable data analysis. However, in very high-dimensional cases such as image feature extraction and recognition, conventional ridge regression or its extensions have the small-class problem, that is, the… read more here.
Keywords: regression; robust regression; generalized robust; subspace learning ... See more keywords
Precision Motion Measurement for Linear Motor Based on FNCC and Phase Difference M-Estimator Robust Regression in a VLSM System
Sign Up to like & getrecommendations! Published in 2022 at "IEEE Transactions on Instrumentation and Measurement"
DOI: 10.1109/tim.2022.3193964
Abstract: This article investigates a low-cost vision-based line-scan measurement (VLSM) system to achieve the high-precision positioning of linear motors. Also, an improved two-step phase difference method is performed for detecting subpixel translation of 1-D multiframe images.… read more here.
Keywords: system; estimator robust; phase difference; robust regression ... See more keywords
A Robust Regression Framework with Laplace Kernel-Induced Loss
Sign Up to like & getrecommendations! Published in 2017 at "Neural Computation"
DOI: 10.1162/neco_a_01002
Abstract: This work proposes a robust regression framework with nonconvex loss function. Two regression formulations are presented based on the Laplace kernel-induced loss (LK-loss). Moreover, we illustrate that the LK-loss function is a nice approximation for… read more here.
Keywords: regression; regression framework; laplace kernel; robust regression ... See more keywords