6 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…
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…
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…
Enhancing Multi-Robot Semantic Navigation Through Multimodal Chain-of-Thought Score Collaboration
Zhixuan Shen, Haonan Luo, Kexun Chen +2
Understanding how humans cooperatively utilize semantic knowledge to explore unfamiliar environments and decide on navigation directions is critical for house service multi-robot s…
CoIFNet: A Unified Framework for Multivariate Time Series Forecasting with Missing Values
Kai Tang, Ji Zhang, Hua Meng +5
Multivariate time series forecasting (MTSF) is a critical task with broad applications in domains such as meteorology, transportation, and economics. Nevertheless, pervasive missin…