Results 41 to 50 of about 379,537 (253)
Distributed Sparse Regression via Penalization
, 2021 We study sparse linear regression over a network of agents, modeled as an undirected graph (with no centralized node). The estimation problem is formulated as the minimization of the sum of the local LASSO loss functions plus a quadratic penalty of the consensus constraint -- the latter being instrumental to obtain distributed solution methods.
Ji, Yao +3 more
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Sparse Support Regression for Image Super-Resolution
IEEE Photonics Journal, 2015 In most optical imaging systems and applications, images with high resolution (HR) are desired and often required. However, charged coupled device (CCD) and complementary metal-oxide semiconductor (CMOS) sensors may be not suitable for some imaging ...
Junjun Jiang +3 more
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Unmixing of Hyperspectral Data Using Spectral Libraries
International Journal of Environment and Geoinformatics, 2020 In hyperspectral images, pixels are found as a mixture of the spectral signatures of several materials, especially when there is an insufficient spatial resolution.
Sefa Küçük, Seniha Esen Yüksel
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Distributed Parallel Sparse Multinomial Logistic Regression
IEEE Access, 2019 Sparse Multinomial Logistic Regression (SMLR) is widely used in the field of image classification, multi-class object recognition, and so on, because it has the function of embedding feature selection during classification.
Dajiang Lei +4 more
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Bilateral Joint-Sparse Regression for Hyperspectral Unmixing
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2021 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 few nonzero rows in the abundance ...
Jie Huang +4 more
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Sequential Co-Sparse Factor Regression [PDF]
Journal of Computational and Graphical Statistics, 2017 In multivariate regression models, a sparse singular value decomposition of the regression component matrix is appealing for reducing dimensionality and facilitating interpretation. However, the recovery of such a decomposition remains very challenging, largely due to the simultaneous presence of orthogonality constraints and co-sparsity regularization.
Aditya, Mishra, Dipak K, Dey, Kun, Chen
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Functional Lagged Regression with Sparse Noisy Observations
, 2020 A functional (lagged) time series regression model involves the regression of scalar response time series on a time series of regressors that consists of a sequence of random functions.
Panaretos, Victor M., Rubín, Tomáš
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Short-Term Traffic Flow Prediction Based on Sparse Regression and Spatio-Temporal Data Fusion
IEEE Access, 2020 Traffic flow forecasting is an important part of intelligent transportation systems. Accurate traffic flow forecasting can not only provide travel advice for people, but also improve traffic management efficiency.
Zengwei Zheng +3 more
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Sparse multiscale gaussian process regression [PDF]
Proceedings of the 25th international conference on Machine learning - ICML '08, 2008 Most existing sparse Gaussian process (g.p.) models seek computational advantages by basing their computations on a set of m basis functions that are the covariance function of the g.p. with one of its two inputs fixed. We generalise this for the case of Gaussian covariance function, by basing our computations on m Gaussian basis functions with ...
Walder, Christian +2 more
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Multilocus genetic analysis of brain images
Frontiers in Genetics, 2011 The quest to identify genes that influence disease is now being extended to find genes that affect biological markers of disease, or endophenotypes. Brain images, in particular, provide exquisitely detailed measures of anatomy, function, and connectivity
Derrek Paul Hibar +5 more
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