Learning Compact Semantic Information for Incomplete Multi-View Missing Multi-Label Classification
Jie Wen, Yadong Liu, Zhanyan Tang, Yuting He, Yulong Chen, Mu Li, Chengliang Liu
Abstract
Multi-view data involves various data forms, such as multi-feature, multi-sequence and multimodal data, providing rich semantic information for downstream tasks. The inherent challenge of incomplete multi-view missing multi-label learning lies in how to effectively utilize limited supervision and insufficient data to learn discriminative representation. Starting from the sufficiency of multi-view shared information for downstream tasks, we argue that the existing contrastive learning paradigms on missing multi-view data show limited consistency representation learning ability, leading to the bottleneck in extracting multi-view shared information. In response, we propose to minimize task-independent redundant information by pursuing the maximization of cross-view mutual information. Additionally, to alleviate the hindrance caused by missing labels, we develop a dual-branch soft pseudo-label cross-imputation strategy to improve classification performance. Extensive experiments on multiple benchmarks validate our advantages and demonstrate strong compatibility with both missing and complete data.
BibTeX
@inproceedings{
wen2025learning,
title={Learning Compact Semantic Information for Incomplete Multi-View Missing Multi-Label Classification},
author={Jie Wen and Yadong Liu and Zhanyan Tang and Yuting He and Yulong Chen and Mu Li and Chengliang Liu},
booktitle={Forty-second International Conference on Machine Learning},
year={2025},
url={https://openreview.net/forum?id=RbsfEuYNTA}
}