A survey on sparse learning models for feature selection
X Li, Y Wang, R Ruiz - IEEE transactions on cybernetics, 2020 - ieeexplore.ieee.org
Feature selection is important in both machine learning and pattern recognition.
Successfully selecting informative features can significantly increase learning accuracy and …
Successfully selecting informative features can significantly increase learning accuracy and …
Integrative high dimensional multiple testing with heterogeneity under data sharing constraints
Identifying informative predictors in a high dimensional regression model is a critical step for
association analysis and predictive modeling. Signal detection in the high dimensional …
association analysis and predictive modeling. Signal detection in the high dimensional …
High-Dimensional Poisson Structural Equation Model Learning via -Regularized Regression
G Park, S Park - Journal of Machine Learning Research, 2019 - jmlr.org
In this paper, we develop a new approach to learning high-dimensional Poisson structural
equation models from only observational data without strong assumptions such as …
equation models from only observational data without strong assumptions such as …
Sparse Poisson regression via mixed-integer optimization
H Saishu, K Kudo, Y Takano - Plos one, 2021 - journals.plos.org
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 …
MIO approach to sparse linear regression was first proposed in the 1970s, but has recently …
High-dimensional regression with a count response
O Zilberman, F Abramovich - arXiv preprint arXiv:2409.08821, 2024 - arxiv.org
We consider high-dimensional regression with a count response modeled by Poisson or
negative binomial generalized linear model (GLM). We propose a penalized maximum …
negative binomial generalized linear model (GLM). We propose a penalized maximum …
Oracle inequalities for weighted group Lasso in high-dimensional Poisson regression model
L Peng - Communications in Statistics-Theory and Methods, 2024 - Taylor & Francis
This article considers the problem of estimating the high-dimensional Poisson regression
model with group sparsity in the parameter vector using the weighted group Lasso method …
model with group sparsity in the parameter vector using the weighted group Lasso method …
Estimating a regression function in exponential families by model selection
J Chen - Bernoulli, 2024 - projecteuclid.org
Estimating a regression function in exponential families by model selection Page 1 Bernoulli
30(2), 2024, 1669–1693 https://doi.org/10.3150/23-BEJ1649 Estimating a regression function in …
30(2), 2024, 1669–1693 https://doi.org/10.3150/23-BEJ1649 Estimating a regression function in …
Variational Inference for Sparse Poisson Regression
M Kharabati, M Amini - arXiv preprint arXiv:2311.01147, 2023 - arxiv.org
We have utilized the non-conjugate VB method for the problem of the sparse Poisson
regression model. To provide an approximated conjugacy in the model, the likelihood is …
regression model. To provide an approximated conjugacy in the model, the likelihood is …
Regularized Poisson regressions predict regional innovation output
L Xiang, H Xuemei, Y Junwen - Journal of Forecasting, 2023 - Wiley Online Library
Regional innovation output is influenced by many factors such as macroeconomic
environments, residents consumption, fixed asset investment, foreign trade, fiscal revenue …
environments, residents consumption, fixed asset investment, foreign trade, fiscal revenue …
基于GPGN 算法的泊松回归稀疏优化
赵子榕， 王思洋 - 应用概率统计, 2025 - aps.ecnu.edu.cn
泊松回归模型作为广义线性回归模型之一, 被广泛应用于计数型数据分析.
随着计算机技术的迅速发展, 获取和存储的变量越来越多, 所建立模型越来越复杂 …
随着计算机技术的迅速发展, 获取和存储的变量越来越多, 所建立模型越来越复杂 …