2 citations · 4 across the 7 of their papers we have counts for
7 papers
Kernel Alignment-based Multi-view Unsupervised Feature Selection with Sample-level Adaptive Graph Learning
Yalan Tan, Yanyong Huang, Zongxin Shen +3
Although multi-view unsupervised feature selection (MUFS) has demonstrated success in dimensionality reduction for unlabeled multi-view data, most existing methods reduce feature r…
Cross-view Joint Learning for Mixed-Missing Multi-view Unsupervised Feature Selection
Zongxin Shen, Yanyong Huang, Dongjie Wang +4
Incomplete multi-view unsupervised feature selection (IMUFS), which aims to identify representative features from unlabeled multi-view data containing missing values, has received…
Beyond Correlation: Causal Multi-View Unsupervised Feature Selection Learning
Zongxin Shen, Yanyong Huang, Bin Wang +3
Multi-view unsupervised feature selection (MUFS) has recently received increasing attention for its promising ability in dimensionality reduction on multi-view unlabeled data. Exis…
Causally-Aware Unsupervised Feature Selection Learning
Zongxin Shen, Yanyong Huang, Dongjie Wang +3
Unsupervised feature selection (UFS) has recently gained attention for its effectiveness in processing unlabeled high-dimensional data. However, existing methods overlook the intri…
Unified View Imputation and Feature Selection Learning for Incomplete Multi-view Data
Yanyong Huang, Zongxin Shen, Tianrui Li +1
Although multi-view unsupervised feature selection (MUFS) is an effective technology for reducing dimensionality in machine learning, existing methods cannot directly deal with inc…
CIMUFS: Complementary and Consensus Learning-based Incomplete Multi-view Unsupervised Feature Selection
Yanyong Huang, Zongxin Shen, Yuxin Cai +4
Multi-view unsupervised feature selection (MUFS) has been demonstrated as an effective technique to reduce the dimensionality of multi-view unlabeled data. The existing methods ass…
Adaptive Graph-based Generalized Regression Model for Unsupervised Feature Selection
Yanyong Huang, Zongxin Shen, Fuxu Cai +2
Unsupervised feature selection is an important method to reduce dimensions of high dimensional data without labels, which is benefit to avoid ``curse of dimensionality'' and improv…