---
title: "Imputation-free Incomplete Multi-view Clustering via Knowledge Distillation"
authors:
  - "Benyu Wu"
  - "Wei Du"
  - "Jun Wang"
  - "Guoxian Yu"
doi: "10.24963/ijcai.2025/731"
url: "https://ijcai.org/proceedings/2025/731"
pdf: "https://ijcai.org/proceedings/2025/0731.pdf"
venue: "Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence"
year: 2025
pages: "6570-6578"
track: "Main Track"
keywords:
  - "Machine Learning: ML: Self-supervised Learning"
  - "Machine Learning: ML: Clustering"
  - "Machine Learning: ML: Unsupervised learning"
  - "Machine Learning: ML: Multi-view learning"
---

# Imputation-free Incomplete Multi-view Clustering via Knowledge Distillation

Benyu Wu, Wei Du, Jun Wang, Guoxian Yu

*Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence*, pages 6570-6578. DOI: [10.24963/ijcai.2025/731](https://doi.org/10.24963/ijcai.2025/731)

## Abstract

Incomplete multi-view data presents a significant challenge for multi-view clustering (MVC). Existing incomplete MVC solutions commonly rely on data imputation to convert incomplete data into complete data. However, this paradigm suffers from the risk of error accumulation when clustering unreliable imputed data, causing suboptimal clustering performance. Moreover, using imputation to fulfill missing data is inefficient, while inferring data categories based solely on the existing views is extremely challenging. To this end, we propose an Imputation-free Incomplete MVC (I2MVC) via pseudo-supervised knowledge distillation. Specifically, I2MVC decomposes the incomplete MVC problem into two tasks: an MVC task for complete data and a pseudo-supervised classification task for fully incomplete data. A self-supervised simple contrastive Teacher network is trained for clustering complete data, and its knowledge is distilled into a lightweight pseudo-supervised Student network. The Student network, unrestricted by view completeness, further guides the clustering of fully incomplete data. Finally, the clustering results from both tasks are merged to generate the final clustering outcome. Experimental results on benchmark datasets demonstrate the effectiveness of I2MVC.