Unsupervised feature selection based on joint spectral learning and general sparse regression
Sign Up to like & getrecommendations! Published in 2019 at "Neural Computing and Applications"
DOI: 10.1007/s00521-019-04117-9
Abstract: Unsupervised feature selection is an important machine learning task since the manual annotated data are dramatically expensive to obtain and therefore very limited. However, due to the existence of noise and outliers in different data… read more here.
Keywords: sparse regression; feature selection;
Sparse regression modeling for short- and long‐term natural gas demand prediction
Sign Up to like & getrecommendations! Published in 2021 at "Annals of Operations Research"
DOI: 10.1007/s10479-021-04089-x
Abstract: The multivariate adaptive regression splines (MARS) model is a flexible non-parametric sparse regression algorithm and provides an excellent promise to data fitting through nonlinear basis functions. During the last decades, it is employed in many… read more here.
Keywords: regression; short long; gas demand; sparse regression ... See more keywords
Novel mixed integer optimization sparse regression approach in chemometrics.
Sign Up to like & getrecommendations! Published in 2020 at "Analytica chimica acta"
DOI: 10.1016/j.aca.2020.08.054
Abstract: Sparse mathematical modelling plays an increasingly important role in chemometrics due to its interpretability and prediction power. While many sparse techniques used in chemometrics rely on L1 penalization to create sparser models, Mixed Integer Optimization… read more here.
Keywords: sparse regression; sparse; mio; integer optimization ... See more keywords
Scaled sequential threshold least-squares (S2TLS) algorithm for sparse regression modeling and flight load prediction
Sign Up to like & getrecommendations! Published in 2019 at "Aerospace Science and Technology"
DOI: 10.1016/j.ast.2018.12.038
Abstract: Abstract This paper presents a Scaled Sequential Thresholded Least Squares (S2TLS) algorithm to construct sparse regression models for flight load prediction. The combined use of a sparsification parameter λ and a magnification factor χ is… read more here.
Keywords: flight; s2tls algorithm; sparse regression; load ... See more keywords
Robust brain MR image compressive sensing via re-weighted total variation and sparse regression.
Sign Up to like & getrecommendations! Published in 2021 at "Magnetic resonance imaging"
DOI: 10.1016/j.mri.2021.10.031
Abstract: Total variation (TV) and non-local self-similarity (NSS) are powerful tools for successfully enhancing compressive sensing performance. However, standard TV approaches often over-smooth detailed edges in the image, due to the uniform regularization of gradient magnitude.… read more here.
Keywords: compressive sensing; image; sparse regression; total variation ... See more keywords
Development of a vision based pose estimation system for robotic machining and improving its accuracy using LSTM neural networks and sparse regression
Sign Up to like & getrecommendations! Published in 2022 at "Robotics and Computer-Integrated Manufacturing"
DOI: 10.1016/j.rcim.2021.102262
Abstract: Abstract In this work, an eye to hand camera based pose estimation system is developed for robotic machining and the accuracy of the estimated pose is improved using two different approaches, namely Long Short Term… read more here.
Keywords: robotic machining; pose estimation; sparse regression;
Robust sparse regression by modeling noise as a mixture of gaussians
Sign Up to like & getrecommendations! Published in 2019 at "Journal of Applied Statistics"
DOI: 10.1080/02664763.2019.1566448
Abstract: ABSTRACT Regression analysis has been proven to be a quite effective tool in a large variety of fields. In many regression models, it is often assumed that noise is with a specific distribution. Although the… read more here.
Keywords: sparse regression; regression; mog lasso; model ... See more keywords
Bilateral Joint-Sparse Regression for Hyperspectral Unmixing
Sign Up to like & getrecommendations! Published in 2021 at "IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing"
DOI: 10.1109/jstars.2021.3115172
Abstract: Sparse hyperspectral unmixing has been a hot topic in recent years. Joint sparsity assumes that each pixel in a small neighborhood of hyperspectral images (HSIs) is composed of the same endmembers, which results in a… read more here.
Keywords: hyperspectral unmixing; bilateral joint; joint sparse; sparse regression ... See more keywords
Superpixel-Based Weighted Collaborative Sparse Regression and Reweighted Low-Rank Representation for Hyperspectral Image Unmixing
Sign Up to like & getrecommendations! Published in 2022 at "IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing"
DOI: 10.1109/jstars.2021.3133428
Abstract: Sparse unmixing with a semisupervised fashion has been applied to hyperspectral remote sensing imagery. However, the imprecise spatial contextual information, the lack of global feature and the high mutual coherences of a spectral library greatly… read more here.
Keywords: sparse regression; collaborative sparse; weighted collaborative; sparse ... See more keywords
A Probabilistic Joint Sparse Regression Model for Semisupervised Hyperspectral Unmixing
Sign Up to like & getrecommendations! Published in 2017 at "IEEE Geoscience and Remote Sensing Letters"
DOI: 10.1109/lgrs.2017.2649418
Abstract: Semisupervised hyperspectral unmixing finds the ratio of spectral library members in the mixture of hyperspectral pixels to find the proportion of pure materials in a natural scene. The two main challenges are noise in observed… read more here.
Keywords: model; sparse regression; hyperspectral unmixing; semisupervised hyperspectral ... See more keywords
Superpixel-Guided Local Sparsity Prior for Hyperspectral Sparse Regression Unmixing
Sign Up to like & getrecommendations! Published in 2022 at "IEEE Geoscience and Remote Sensing Letters"
DOI: 10.1109/lgrs.2022.3218730
Abstract: Sparse regression relaxes the difficulties of blind unmixing of hyperspectral data thanks to the spectral library. Many investigations, however, attach importance to global priors such as sparsity and low rankness. This letter proposes a local-global-based… read more here.
Keywords: sparsity; sparse regression; local sparsity; regression unmixing ... See more keywords