---
title: "Content-style Disentanglement Guided Representation Learning for Deep Incomplete Multi-view Clustering"
authors:
  - "Enze Ji"
  - "Meng Liu"
  - "Yuzhe Li"
  - "Zhikui Chen"
doi: "10.24963/ijcai.2026/489"
url: "https://ijcai.org/proceedings/2026/489"
pdf: "https://ijcai.org/proceedings/2026/0489.pdf"
venue: "Proceedings of the Thirty-Fifth International Joint Conference on Artificial Intelligence"
year: 2026
pages: "4394-4402"
track: "Main Track"
keywords:
  - "Machine Learning: Clustering"
  - "Machine Learning: Multi-view learning"
  - "Machine Learning: Deep learning architectures"
---

# Content-style Disentanglement Guided Representation Learning for Deep Incomplete Multi-view Clustering

Enze Ji, Meng Liu, Yuzhe Li, Zhikui Chen

*Proceedings of the Thirty-Fifth International Joint Conference on Artificial Intelligence*, pages 4394-4402. DOI: [10.24963/ijcai.2026/489](https://doi.org/10.24963/ijcai.2026/489)

## Abstract

Deep incomplete multi-view clustering methods can mine patterns of incomplete multi-view data without labels, gaining great attention in various domains. However, current methods are obsessed with aligning view-specific representations from available samples with complete views to learn view-invariant information for missing data recovery, ignoring fruitful view-complementary information beneficial to data imputations. Meanwhile, such the view-invariant learning often leads to the problem of dominant view dependency that models tend to over-rely on views with clear clustering structures and neglect weaker ones, causing a performance bottleneck. Therefore, a content-style disentanglement guided representation learning is proposed for the incomplete multi-view clustering (CSMVC). Specifically, CSMVC designs a view-specific dual-representation learning architecture that extracts view-invariant content representations and view-specific style representations from self-supervised reconstructions of each view with the guidance of the Hilbert schmidt independence criterion. Then, it performs a content-centered style-modulated representation imputation mechanism to infer missing data via fully utilizing inter-view complementary and consistent information. Meanwhile, it devises a cross-view consensus structure mining strategy to capture the clustering structure with intra-cluster compactness and inter-cluster separability from attention-based fusion representations that adaptively aggregate content representations and style representations of each view. Finally, comprehensive experiments show that CSMVC outperforms state-of-the-art methods.