1 citations · 1 across the 9 of their papers we have counts for
10 papers
Causally Sufficient and Necessary Feature Expansion for Class-Incremental Learning
Zhen Zhang, Jielei Chu, Jiangtao Hu +4
Current expansion-based methods for Class Incremental Learning (CIL) effectively mitigate catastrophic forgetting by freezing old features. However, such task-specific features lea…
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…
Joint Learning of Unsupervised Multi-view Feature and Instance Co-selection with Cross-view Imputation
Yuxin Cai, Yanyong Huang, Jinyuan Chang +3
Feature and instance co-selection, which aims to reduce both feature dimensionality and sample size by identifying the most informative features and instances, has attracted consid…
Structure-aware Hybrid-order Similarity Learning for Multi-view Unsupervised Feature Selection
Lin Xu, Ke Li, Dongjie Wang +3
Multi-view unsupervised feature selection (MUFS) has recently emerged as an effective dimensionality reduction method for unlabeled multi-view data. However, most existing methods…
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…
TRUST-FS: Tensorized Reliable Unsupervised Multi-View Feature Selection for Incomplete Data
Minghui Lu, Yanyong Huang, Minbo Ma +4
Multi-view unsupervised feature selection (MUFS), which selects informative features from multi-view unlabeled data, has attracted increasing research interest in recent years. Alt…
CONDEN-FI: Consistency and Diversity Learning-based Multi-View Unsupervised Feature and In-stance Co-Selection
Yanyong Huang, Yuxin Cai, Dongjie Wang +2
The objective of multi-view unsupervised feature and instance co-selection is to simultaneously iden-tify the most representative features and samples from multi-view unlabeled dat…
Adaptive Collaborative Correlation Learning-based Semi-Supervised Multi-Label Feature Selection
Li Yang, Yanyong Huang, Dongjie Wang +4
Semi-supervised multi-label feature selection has recently been developed to solve the curse of dimensionality problem in high-dimensional multi-label data with certain samples mis…
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…