Results 241 to 250 of about 379,537 (253)
Some of the next articles are maybe not open access.
INFORMS Journal on Computing, 2021
We consider the problem of best [Formula: see text]-subset convex regression using [Formula: see text] observations in [Formula: see text] variables. For the case without sparsity, we develop a scalable algorithm for obtaining high quality solutions in practical times that compare favorably with other state of the art methods.
Dimitris Bertsimas, Nishanth Mundru
openaire +1 more source
We consider the problem of best [Formula: see text]-subset convex regression using [Formula: see text] observations in [Formula: see text] variables. For the case without sparsity, we develop a scalable algorithm for obtaining high quality solutions in practical times that compare favorably with other state of the art methods.
Dimitris Bertsimas, Nishanth Mundru
openaire +1 more source
Multitarget Sparse Latent Regression
IEEE Transactions on Neural Networks and Learning Systems, 2018Multitarget regression has recently generated intensive popularity due to its ability to simultaneously solve multiple regression tasks with improved performance, while great challenges stem from jointly exploring inter-target correlations and input-output relationships.
Xiantong Zhen +6 more
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Marginalized lasso in sparse regression
Journal of the Korean Statistical Society, 2019zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Lee, Seokho, Kim, Seonhwa
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Sparse Least squares support vector regression via Multiresponse Sparse Regression
2016 International Joint Conference on Neural Networks (IJCNN), 2016Least square support vector machines (LSSVMs) are an alternative to SVMs because the training process for LSSVMs is based on solving a linear equation system while the training process for SVMs relies on solving a quadratic programming optimization problem.
David Clifte da S. Vieira +2 more
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2020
A matrix or vector is said to be sparse when it includes a number of zero elements. Hence, the term sparse estimation refers to estimating a number of parameters as zeros. The developments in multivariate analysis procedures with sparse estimation started from modifications to the multiple regression analysis introduced in Chap. 4.
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A matrix or vector is said to be sparse when it includes a number of zero elements. Hence, the term sparse estimation refers to estimating a number of parameters as zeros. The developments in multivariate analysis procedures with sparse estimation started from modifications to the multiple regression analysis introduced in Chap. 4.
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Sparse and nonnegative sparse D-MORPH regression
Journal of Mathematical Chemistry, 2015zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Li, Genyuan +3 more
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Sparse regression as a sparse eigenvalue problem
2008 Information Theory and Applications Workshop, 2008We extend the l0-norm ldquosubspectralrdquo algorithms developed for sparse-LDA (Moghaddam, 2006) and sparse-PCA (Moghaddam, 2006) to more general quadratic costs such as MSE in linear (or kernel) regression. The resulting ldquosparse least squaresrdquo (SLS) problem is also NP-hard, by way of its equivalence to a rank-1 sparse eigenvalue problem ...
Baback Moghaddam +3 more
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