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Toward Comprehensive Information-Theoretic Multi-View Learning.

Long Shi, Yunshan Ye, Wenjie Wang

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society

|May 4, 2026

Summary

This summary is machine-generated.

This study introduces CIML, a new multi-view learning framework that utilizes both common and unique information for better predictions. It moves beyond the redundancy assumption, showing superior performance in experiments.

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Area of Science:

Machine Learning
Information Theory
Computer Science

Background:

Multi-view learning methods often assume redundancy, focusing only on common information between views.
This assumption overlooks the predictive value of unique information present in individual views.

Purpose of the Study:

To propose a novel information-theoretic multi-view learning framework (CIML) that leverages both common and unique information.
To develop a method that discards the restrictive multi-view redundancy assumption.

Main Methods:

CIML maximizes Gacs-Korner common information for shared features and uses Information Bottleneck (IB) for task-relevant compression.
IB is applied to unique representation learning, compressing unique information while minimizing its correlation with common and other unique representations.

Main Results:

Theoretically proved the predictive sufficiency of the learned joint representation.
Extensive experiments demonstrated CIML's superiority over state-of-the-art multi-view learning methods.

Conclusions:

CIML offers a more comprehensive approach to multi-view learning by considering both common and unique information.
The framework advances information-theoretic multi-view learning by moving beyond the redundancy assumption.