6 papers
Sparse insensitive zone bounded asymmetric elastic net support vector machines for pattern classification
Haiyan Du, Hu Yang
Existing support vector machines(SVM) models are sensitive to noise and lack sparsity, which limits their performance. To address these issues, we combine the elastic net loss with…
Robust support vector model based on bounded asymmetric elastic net loss for binary classification
Haiyan Du, Hu Yang
In this paper, we propose a novel bounded asymmetric elastic net () loss function and combine it with the support vector machine (SVM), resulting in the BAEN-SVM. The $L_…
An Interpretable and Stable Framework for Sparse Principal Component Analysis
Ying Hu, Hu Yang
Sparse principal component analysis (SPCA) addresses the poor interpretability and variable redundancy often encountered by principal component analysis (PCA) in high-dimensional d…
SPPCSO: Adaptive Penalized Estimation Method for High-Dimensional Correlated Data
Ying Hu, Hu Yang
With the rise of high-dimensional correlated data, multicollinearity poses a significant challenge to model stability, often leading to unstable estimation and reduced predictive a…
Sparse Optimization for Transfer Learning: A L0-Regularized Framework for Multi-Source Domain Adaptation
Chenqi Gong, Hu Yang
This paper explores transfer learning in heterogeneous multi-source environments with distributional divergence between target and auxiliary domains. To address challenges in stati…
Communication-Efficient l_0 Penalized Least Square
Chenqi Gong, Hu Yang
In this paper, we propose a communication-efficient penalized regression algorithm for high-dimensional sparse linear regression models with massive data. This approach incorporate…