Results 11 to 20 of about 379,537 (253)
Incremental Sparse Bayesian Ordinal Regression [PDF]
Neural Networks, 2018 Ordinal Regression (OR) aims to model the ordering information between different data categories, which is a crucial topic in multi-label learning. An important class of approaches to OR models the problem as a linear combination of basis functions that ...
de Rijke, Maarten, Li, Chang
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Sparse Multivariate Factor Regression [PDF]
2016 IEEE Statistical Signal Processing Workshop (SSP), 2016 We consider the problem of multivariate regression in a setting where the relevant predictors could be shared among different responses. We propose an algorithm which decomposes the coefficient matrix into the product of a long matrix and a wide matrix ...
Coates, Mark, Kharratzadeh, Milad
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Scaled Sparse Linear Regression [PDF]
Biometrika, 2012 Scaled sparse linear regression jointly estimates the regression coefficients and noise level in a linear model. It chooses an equilibrium with a sparse regression method by iteratively estimating the noise level via the mean residual square and scaling ...
Sun, Tingni, Zhang, Cun-Hui
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Detection boundary in sparse regression [PDF]
Electronic Journal of Statistics, 2010 We study the problem of detection of a p-dimensional sparse vector of parameters in the linear regression model with Gaussian noise. We establish the detection boundary, i.e., the necessary and sufficient conditions for the possibility of successful ...
Ingster, Yuri I. +2 more
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Optimal Sparse Regression Trees. [PDF]
Proc AAAI Conf Artif Intell, 2023 Regression trees are one of the oldest forms of AI models, and their predictions can be made without a calculator, which makes them broadly useful, particularly for high-stakes applications. Within the large literature on regression trees, there has been little effort towards full provable optimization, mainly due to the computational hardness of the ...
Zhang R, Xin R, Seltzer M, Rudin C.
europepmc +4 more sources
Leukemia prediction using sparse logistic regression.
PLoS ONE, 2013 We describe a supervised prediction method for diagnosis of acute myeloid leukemia (AML) from patient samples based on flow cytometry measurements. We use a data driven approach with machine learning methods to train a computational model that takes in ...
Tapio Manninen +3 more
doaj +5 more sources
Bayesian Factor-adjusted Sparse Regression. [PDF]
J Econom, 2022 This paper investigates the high-dimensional linear regression with highly correlated covariates. In this setup, the traditional sparsity assumption on the regression coefficients often fails to hold, and consequently many model selection procedures do not work.
Fan J, Jiang B, Sun Q.
europepmc +5 more sources
Sparse Quantile Regression [PDF]
Journal of Econometrics, 2020 We consider both $\ell _{0}$-penalized and $\ell _{0}$-constrained quantile regression estimators. For the $\ell _{0}$-penalized estimator, we derive an exponential inequality on the tail probability of excess quantile prediction risk and apply it to obtain non-asymptotic upper bounds on the mean-square parameter and regression function estimation ...
Lee, Sokbae (Simon), Chen, Le-Yu
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Constrained sparse Galerkin regression [PDF]
Journal of Fluid Mechanics, 2018 The sparse identification of nonlinear dynamics (SINDy) is a recently proposed data-driven modelling framework that uses sparse regression techniques to identify nonlinear low-order models. With the goal of low-order models of a fluid flow, we combine this approach with dimensionality reduction techniques (e.g.
Loiseau, Jean-Christophe +1 more
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Foundations and Trends® in Communications and Information Theory, 2019 Developing computationally-efficient codes that approach the Shannon-theoretic limits for communication and compression has long been one of the major goals of information and coding theory. There have been significant advances towards this goal in the last couple of decades, with the emergence of turbo codes, sparse-graph codes, and polar codes. These
Venkataramanan, Ramji +2 more
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