Results 21 to 30 of about 379,537 (253)
New Developments in Sparse PLS Regression
Frontiers in Applied Mathematics and Statistics, 2021 Methods based on partial least squares (PLS) regression, which has recently gained much attention in the analysis of high-dimensional genomic datasets, have been developed since the early 2000s for performing variable selection.
Jérémy Magnanensi +6 more
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Greedy permanent magnet optimization
Nuclear Fusion, 2023 A number of scientific fields rely on placing permanent magnets in order to produce a desired magnetic field. We have shown in recent work that the placement process can be formulated as sparse regression.
Alan A. Kaptanoglu +2 more
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Principal component‐guided sparse regression [PDF]
Canadian Journal of Statistics, 2021 AbstractWe propose a new method for supervised learning, the “principal components lasso” (“pcLasso”). It combines the lasso (ℓ1) penalty with a quadratic penalty that shrinks the coefficient vector toward the feature matrix's leading principal components (PCs). pcLasso can be especially powerful if the features are preassigned to groups. In that case,
Jingyi K. Tay +2 more
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Sparse Multivariate Gaussian Mixture Regression [PDF]
IEEE Transactions on Neural Networks and Learning Systems, 2015 Fitting a multivariate Gaussian mixture to data represents an attractive, as well as challenging problem, in especial when sparsity in the solution is demanded. Achieving this objective requires the concurrent update of all parameters (weight, centers, and precisions) of all multivariate Gaussian functions during the learning process. Such is the focus
Weruaga Prieto, Luis +1 more
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Sparse hierarchical regression with polynomials [PDF]
Machine Learning, 2020 We present a novel method for exact hierarchical sparse polynomial regression. Our regressor is that degree $r$ polynomial which depends on at most $k$ inputs, counting at most $\ell$ monomial terms, which minimizes the sum of the squares of its prediction errors.
Dimitris Bertsimas, Bart Van Parys
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Sparse regression for extreme values [PDF]
Electronic Journal of Statistics, 2021 We study the problem of selecting features associated with extreme values in high dimensional linear regression. Normally, in linear modeling problems, the presence of abnormal extreme values or outliers is considered an anomaly which should either be removed from the data or remedied using robust regression methods.
Chang, Andersen +2 more
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Sparse Poisson regression via mixed-integer optimization.
PLoS ONE, 2021 We present a mixed-integer optimization (MIO) approach to sparse Poisson regression. The MIO approach to sparse linear regression was first proposed in the 1970s, but has recently received renewed attention due to advances in optimization algorithms and ...
Hiroki Saishu, Kota Kudo, Yuichi Takano
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Applied Sciences, 2022 In recent years, the rapid growth of computing technology has enabled identifying mathematical models for vibration systems using measurement data instead of domain knowledge.
Yaxiong Ren +2 more
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On sparse optimal regression trees
European Journal of Operational Research, 2022 In this paper, we model an optimal regression tree through a continuous optimization problem, where a compromise between prediction accuracy and both types of sparsity, namely local and global, is sought. Our approach can accommodate important desirable properties for the regression task, such as cost-sensitivity and fairness.
Blanquero, Rafael +3 more
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Distributed Sparse Linear Regression
IEEE Transactions on Signal Processing, 2010 The Lasso is a popular technique for joint estimation and continuous variable selection, especially well-suited for sparse and possibly under-determined linear regression problems. This paper develops algorithms to estimate the regression coefficients via Lasso when the training data are distributed across different agents, and their communication to a
Mateos, Gonzalo +2 more
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