No Data Wasted: A Semi-supervised Generative Model for Incomplete Multi-view Data Integration with Missing Labels
cs.LG, cs.AI
Submitted: 2025-08-15
Updated: 2026-09-01
Code: https://github.com/chl8856/DeepIMV
License: http://creativecommons.org/licenses/by/4.0/
The gist: Multi-view learning is widely applied to real-life datasets, but it often suffers from both missing views and missing labels.
Terminology
Abstract
Multi-view learning is widely applied to real-life datasets, but it often suffers from both missing views and missing labels. Prior probabilistic approaches addressed the missing view problem by using a product-of-experts scheme to aggregate representations from present views and achieved superior performance over deterministic classifiers, using the information bottleneck (IB) principle. However, the IB framework is inherently fully supervised and cannot leverage unlabeled data. In this work, we propose a semi-supervised generative model that utilizes both labeled and unlabeled samples in a unified framework. Our method maximizes the likelihood of unlabeled samples to learn a latent space shared with the IB on labeled data. We also include modality-specific information in likelihood modeling and perform cross-view mutual information maximization in the shared latent space to enhance the extraction of shared information across views. Compared to existing approaches, our model achieves better predictive and generation performance on complex datasets with missing views and limited labeled samples.
Sources
- Deep Residual Learning for Image Recognition
- The Kinetics Human Action Video Dataset
- Representation Learning with Contrastive Predictive Coding
- Joint Multimodal Learning with Deep Generative Models
- EMP-SSL: Towards Self-Supervised Learning in One Training Epoch
- Deep Variational Canonical Correlation Analysis
- CLCLSA: Cross-omics Linked embedding with Contrastive Learning and Self Attention for multi-omics integration with incomplete multi-omics data
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