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ICIRD: Information-Principled Deep Clustering for Invariant, Redundancy-Reduced and Discriminative Cluster

Aiyu Zheng^1,2, Robert M X Wu³, Yupeng Wang¹

¹School of Electronic Information and Engineering, Taiyuan University of Science and Technology, Taiyuan 030024, China.

Entropy (Basel, Switzerland)

|December 24, 2025

Summary

This summary is machine-generated.

This study introduces ICIRD, a novel deep clustering framework that enhances data grouping by optimizing cluster probability distributions. ICIRD reduces ambiguity and redundancy for more accurate and invariant data clustering.

Keywords:

contrastive learning deep clustering discriminative distribution sharpness discriminative learning information-principled deep clustering multi-view inter-cluster distribution redundancy reduction

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Area of Science:

Artificial Intelligence
Machine Learning
Data Science

Background:

Deep clustering methods often struggle with ambiguous and redundant cluster assignments.
Existing approaches overlook the information characteristics of cluster probability distributions, especially with augmented data views.

Purpose of the Study:

To propose an information-principled deep clustering framework, ICIRD, for learning invariant, redundancy-reduced, and discriminative cluster probability distributions.
To address limitations in current deep clustering techniques regarding assignment certainty and cross-view consistency.

Main Methods:

ICIRD employs conditional entropy minimization to enhance assignment certainty and discriminability.
Inter-cluster mutual information minimization reduces redundancy and sharpens cluster separability.
Cross-view mutual information maximization enforces semantic consistency across augmented data views, complemented by a contrastive representation mechanism.

Main Results:

ICIRD demonstrates superior performance compared to existing deep clustering methods across five benchmark image datasets.
The framework shows particular effectiveness on fine-grained datasets like CIFAR-100 and ImageNet-Dogs.
Experiments confirm the ability of ICIRD to jointly optimize representations and cluster probability distributions in an information-regularized manner.

Conclusions:

ICIRD offers a principled approach to deep clustering by focusing on information characteristics of probability distributions.
The proposed framework effectively learns invariant, redundancy-reduced, and discriminative cluster assignments.
ICIRD advances the state-of-the-art in deep clustering, especially for complex, fine-grained image datasets.