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arXiv · 2609.35212

Adversarial Consistency-Guided Representation Learning for Multi-view Clustering

Yuchen Lin·Kunpeng Xu·Ying Fang·Lifei Chen
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Abstract

Multi-view clustering aims to capture cross-view consistency while exploiting view-specific information. However, shared representations learned to capture cross-view consistency may still retain view-identifying information, potentially compromising the consistency of cross-view clustering structures. To address this issue, we propose ACGRL, an adversarial consistency-guided representation learning framework for multi-view clustering. ACGRL employs a gradient-reversal view discriminator to reduce view identifiability and obtain invariant reference representations. These representations are then frozen to provide fixed references for disentangling view-specific information from cross-view common information in the subsequent learning stage. The fixed reference representations are concatenated with the learned view-specific representations for reconstruction and clustering, with cross-view cluster alignment encouraging consistent clustering assignments. Experiments on four benchmark datasets demonstrate the superior clustering performance of ACGRL compared with representative multi-view clustering methods.

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Yuchen Lin, Kunpeng Xu, Ying Fang, Lifei Chen. 2026-09-28. Adversarial Consistency-Guided Representation Learning for Multi-view Clustering. https://arxiv.org/abs/2609.35212

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