Generative Incomplete Multiview Representation Learning With Learnable Graph

Area of Science:

• Machine Learning
• Computer Science
• Data Science

Background:

• Incomplete multiview data presents significant challenges for representation learning due to missing views and cross-view inconsistencies.
• Existing deep generative models for data imputation often rely on rigid assumptions or are task-specific, limiting their adaptability.

Purpose of the Study:

• To propose a learnable graph-based generative representation learning framework for handling incomplete multiview data.
• To jointly model multiview dependencies and missing data using an adaptive topological structure.

Main Methods:

• Developed a framework that adaptively fuses view-specific graphs into a unified structure to capture shared and view-specific relational patterns.
• Integrated message propagation over adaptive graph structures with adversarial representation learning for feature reconstruction and cross-view alignment.

Main Results:

• The proposed model demonstrated reliable feature reconstruction and consistent cross-view representations in incomplete settings.
• Achieved superior performance compared to existing approaches on multiview semi-supervised classification benchmarks.

Conclusions:

• The learnable graph-based generative framework is robust and effective for handling incomplete multiview data.
• The method offers a more adaptable and powerful solution for representation learning with missing data across multiple views.