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Nonnegative Elastic Net and application in index tracking [PDF]

Applied Mathematics and Computation, 2014
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Yuehan Yang
exaly   +4 more sources

Elastic net orthogonal forward regression [PDF]

Neurocomputing, 2015
An efficient two-level model identification method aiming at maximising a model׳s generalisation capability is proposed for a large class of linear-in-the-parameters models from the observational data. A new elastic net orthogonal forward regression (ENOFR) algorithm is employed at the lower level to carry out simultaneous model selection and elastic ...
Xia Hong 0001, Sheng Chen 0001
openaire   +3 more sources

Analysis of Elastic-Net Regularization [PDF]

, 2008
We give a review on some recent results about the consistency of the elastic-net algorithm for sparse recovering in the context of learning ...
C. De Mol, DE VITO, ERNESTO, ROSASCO, LORENZO   +2 more
openaire   +2 more sources

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The Bayesian elastic net regression

Communications in Statistics - Simulation and Computation, 2017
A Bayesian elastic net approach is presented for variable selection and coefficient estimation in linear regression models.
Rahim Alhamzawi, Haithem Taha Mohammad Ali   +1 more
openaire   +1 more source

Universality of the elastic net error

2017 IEEE International Symposium on Information Theory (ISIT), 2017
We consider the problem of reconstructing a vector x 0 ∊ Rn from noisy linear observations y = Ax o + w, where A ∊ Rm×n is a known operator and w is a noise vector, using the elastic net method. Assuming that A is random with independent and identically distributed entries, and under suitable moment conditions, we prove the following universality ...
Andrea Montanari, Phan-Minh Nguyen
openaire   +1 more source

A Parallel Elastic Net Clustering Algorithm

2018 IEEE International Conference on Smart Internet of Things (SmartIoT), 2018
The elastic net clustering algorithm (ENCA) can typically provide an effective way for classifying non-linearly separable data. However, the computation time it takes will be significantly increased for large datasets. To deal with this issue, a parallel version of the ENCA, built on the Apache Spark framework, called parallel elastic net clustering ...
Tzu-Yi Feng, Chun-Wei Tsai, Ming-Chao Chiang, Chu-Sing Yang   +3 more
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Equilibrium of elastic nets

Philosophical Transactions of the Royal Society of London. Series A: Physical and Engineering Sciences, 1991
Abstract A general equilibrium theory for nets constructed from two families of perfectly flexible elastic fibres is presented. The fibres are assumed to be continuously distributed and to offer negligible resistance to shear distortion.
D. J. Steigmann, A. C. Pipkin
openaire   +1 more source

Latent Elastic-Net Transfer Learning

IEEE Transactions on Image Processing, 2020
Subspace learning based transfer learning methods commonly find a common subspace where the discrepancy of the source and target domains is reduced. The final classification is also performed in such subspace. However, the minimum discrepancy does not guarantee the best classification performance and thus the common subspace may be not the best ...
Na Han, Jigang Wu, Xiaozhao Fang, Shengli Xie, Shanhua Zhan, Kan Xie, Xuelong Li 0001   +6 more
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Manifold elastic net for sparse learning

2009 IEEE International Conference on Systems, Man and Cybernetics, 2009
In this paper, we present the manifold elastic net (MEN) for sparse variable selection. MEN combines merits of the manifold regularization and the elastic net regularization, so it considers both the nonlinear manifold structure of a dataset and the sparse property of the redundant data representation.
Tianyi Zhou 0001, Dacheng Tao
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Scaled and square-root elastic net

2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2017
In scaled lasso, the unknown regression coefficients and the scale parameter of the error distribution are estimated jointly. In lasso, the optimal penalty parameter is well-known to depend on the error scale, and it is therefore typically chosen using cross-validation.
Ollila, Esa, Raninen, Elias
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