127 citations · 372 across the 10 of their papers we have counts for
10 papers
Large-scale Feature Selection of Risk Genetic Factors for Alzheimer's Disease via Distributed Group Lasso Regression
Qingyang Li, Dajiang Zhu, Jie Zhang +6
Genome-wide association studies (GWAS) have achieved great success in the genetic study of Alzheimer's disease (AD). Collaborative imaging genetics studies across different researc…
Large-scale Collaborative Imaging Genetics Studies of Risk Genetic Factors for Alzheimer's Disease Across Multiple Institutions
Qingyang Li, Tao Yang, Liang Zhan +6
Genome-wide association studies (GWAS) offer new opportunities to identify genetic risk factors for Alzheimer's disease (AD). Recently, collaborative efforts across different insti…
Scaling Up Sparse Support Vector Machines by Simultaneous Feature and Sample Reduction
Weizhong Zhang, Bin Hong, Wei Liu +4
Sparse support vector machine (SVM) is a popular classification technique that can simultaneously learn a small set of the most interpretable features and identify the support vect…
Safe Screening for Multi-Task Feature Learning with Multiple Data Matrices
Jie Wang, Jieping Ye
Multi-task feature learning (MTFL) is a powerful technique in boosting the predictive performance by learning multiple related classification/regression/clustering tasks simultaneo…
Two-Layer Feature Reduction for Sparse-Group Lasso via Decomposition of Convex Sets
Jie Wang, Jieping Ye
Sparse-Group Lasso (SGL) has been shown to be a powerful regression technique for simultaneously discovering group and within-group sparse patterns by using a combination of the $\…
Scaling SVM and Least Absolute Deviations via Exact Data Reduction
Jie Wang, Peter Wonka, Jieping Ye
The support vector machine (SVM) is a widely used method for classification. Although many efforts have been devoted to develop efficient solvers, it remains challenging to apply S…
A Safe Screening Rule for Sparse Logistic Regression
Jie Wang, Jiayu Zhou, Jun Liu +2
The l1-regularized logistic regression (or sparse logistic regression) is a widely used method for simultaneous classification and feature selection. Although many recent efforts h…
Efficient Mixed-Norm Regularization: Algorithms and Safe Screening Methods
Jie Wang, Jun Liu, Jieping Ye
Sparse learning has recently received increasing attention in many areas including machine learning, statistics, and applied mathematics. The mixed-norm regularization based on the…
Safe Screening With Variational Inequalities and Its Application to LASSO
Jun Liu, Zheng Zhao, Jie Wang +1
Sparse learning techniques have been routinely used for feature selection as the resulting model usually has a small number of non-zero entries. Safe screening, which eliminates th…
Lasso Screening Rules via Dual Polytope Projection
Jie Wang, Peter Wonka, Jieping Ye
Lasso is a widely used regression technique to find sparse representations. When the dimension of the feature space and the number of samples are extremely large, solving the Lasso…