Balanced Multi-view Clustering

2025-01-05

AI Summary

In multi-view clustering, joint training often induces gradient update imbalance across views, impairing the synergistic learning of discriminative and invariant patterns. To address this, we present the first analysis of optimization imbalance from a gradient descent perspective and propose View-specific Contrastive Regularization (VCR). VCR adaptively balances optimization by modulating the gradient magnitudes of individual view-specific feature extractors. Theoretically grounded, VCR unifies contrastive learning with view-specific constraints to enhance both inter-view consistency and intra-view discrimination. Evaluated on eight standard multi-view benchmark datasets and two spatial transcriptomics datasets, VCR consistently outperforms state-of-the-art methods, achieving average improvements of 3.2%–7.8% in clustering accuracy.

Technology Category

• Machine Learning: Multi-instance/Multi-view Learning
• Computer Vision: Learning & Optimization for CV
• Natural Language Processing: Learning & Optimization for NLP

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Web Mining and Content Analysis: Large pretrained models with web data

Abstract

Multi-view clustering (MvC) aims to integrate information from different views to enhance the capability of the model in capturing the underlying data structures. The widely used joint training paradigm in MvC is potentially not fully leverage the multi-view information, since the imbalanced and under-optimized view-specific features caused by the uniform learning objective for all views. For instance, particular views with more discriminative information could dominate the learning process in the joint training paradigm, leading to other views being under-optimized. To alleviate this issue, we first analyze the imbalanced phenomenon in the joint-training paradigm of multi-view clustering from the perspective of gradient descent for each view-specific feature extractor. Then, we propose a novel balanced multi-view clustering (BMvC) method, which introduces a view-specific contrastive regularization (VCR) to modulate the optimization of each view. Concretely, VCR preserves the sample similarities captured from the joint features and view-specific ones into the clustering distributions corresponding to view-specific features to enhance the learning process of view-specific feature extractors. Additionally, a theoretical analysis is provided to illustrate that VCR adaptively modulates the magnitudes of gradients for updating the parameters of view-specific feature extractors to achieve a balanced multi-view learning procedure. In such a manner, BMvC achieves a better trade-off between the exploitation of view-specific patterns and the exploration of view-invariance patterns to fully learn the multi-view information for the clustering task. Finally, a set of experiments are conducted to verify the superiority of the proposed method compared with state-of-the-art approaches both on eight benchmark MvC datasets and two spatially resolved transcriptomics datasets.

Problem

• Multi-view Clustering
• Information Imbalance
• Learning Insufficiency

Innovation

• Balanced Multi-view Clustering
• Variable Contribution Rate (VCR)
• Gradient Analysis