Information theory-guided heuristic progressive multi-view coding.

Area of Science:

• Artificial Intelligence
• Machine Learning
• Information Theory

Background:

• Multi-view representation learning seeks comprehensive data insights from diverse perspectives.
• Current contrastive learning methods face challenges with view-specific noise, fake negative pairs, and uniform similarity measures.
• A generalized theoretical framework for self-supervised multi-view learning, especially beyond two views, remains underexplored.

Purpose of the Study:

• To develop a novel information-theoretical framework for generalized multi-view learning.
• To propose a new multi-view coding method, Information theory-guided Progressive Multi-view Coding (IPMC), based on this framework.
• To address limitations in existing pairwise contrastive learning approaches for multi-view data.

Main Methods:

• Developed a three-tier progressive architecture: distribution-tier, set-tier, and instance-tier.
• Aligned view distributions in the distribution-tier to mitigate view-specific noise.
• Implemented self-adjusted contrasting pools with a view filter in the set-tier.
• Utilized a unified loss function in the instance-tier to refine representations and reduce gradient interference.

Main Results:

• The proposed IPMC method demonstrates superior performance compared to state-of-the-art techniques.
• Theoretical analysis and empirical evaluations validate the effectiveness of the IPMC framework.
• IPMC successfully filters view-specific noise and optimizes the handling of positive and negative pairs in contrastive learning.

Conclusions:

• The novel information-theoretical framework provides a robust foundation for generalized multi-view learning.
• IPMC offers a significant advancement in learning effective representations from multiple data views.
• The progressive, information-theory-guided approach enhances the scalability and accuracy of multi-view representation learning.