# Incomplete Multi-view Clustering via Cross-view Relation Transfer

arXiv: https://arxiv.org/abs/2112.00739 · HTML: https://mlsanity.com/paper/2112.00739/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 66 references that are in the map.
- First posted on arXiv 1 Dec 2021.

## Builds on (most cited first, 14 of 14)

- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [Variational Graph Auto-Encoders](https://mlsanity.com/paper/1611.07308/) (2016)
- [Attributed Graph Clustering: A Deep Attentional Embedding Approach](https://mlsanity.com/paper/1906.06532/) (2019)
- [Structural Deep Clustering Network](https://mlsanity.com/paper/2002.01633/) (2020)
- [Relation-Aware Graph Attention Network for Visual Question Answering](https://mlsanity.com/paper/1903.12314/) (2019)
- [Deep Partial Multi-View Learning](https://mlsanity.com/paper/2011.06170/) (2020)
- [Doubly Aligned Incomplete Multi-view Clustering](https://mlsanity.com/paper/1903.02785/) (2019)
- [Graph Cross Networks with Vertex Infomax Pooling](https://mlsanity.com/paper/2010.01804/) (2020)
- [Generative Partial Multi-View Clustering](https://mlsanity.com/paper/2003.13088/) (2020)
- [XAI Beyond Classification: Interpretable Neural Clustering](https://mlsanity.com/paper/1808.07292/) (2018)
- [One-Pass Incomplete Multi-view Clustering](https://mlsanity.com/paper/1903.00637/) (2019)
- [Clustering on Multiple Incomplete Datasets via Collective Kernel Learning](https://mlsanity.com/paper/1310.1177/) (2013)

## Built on by (most cited first, 10 of 10)

- [Pro-tuning: Unified Prompt Tuning for Vision Tasks](https://mlsanity.com/paper/2207.14381/) (2022)
- [Deep Incomplete Multi-view Clustering with Cross-view Partial Sample and Prototype Alignment](https://mlsanity.com/paper/2303.15689/) (2023)
- [Imputation-free and Alignment-free: Incomplete Multi-view Clustering Driven by Consensus Semantic Learning](https://mlsanity.com/paper/2505.11182/) (2025)
- [Incomplete Multi-view Clustering via Hierarchical Semantic Alignment and Cooperative Completion](https://mlsanity.com/paper/2510.13887/) (2025)
- [Deep Incomplete Multi-view Clustering with Distribution Dual-Consistency Recovery Guidance](https://mlsanity.com/paper/2503.11017/) (2025)
- [Regularized Contrastive Partial Multi-view Outlier Detection](https://mlsanity.com/paper/2408.07819/) (2024)
- [Federated Incomplete Multi-view Clustering with Globally Fused Graph Guidance](https://mlsanity.com/paper/2506.15703/) (2025)
- [Straight-Path Flow Matching for Incomplete Multi-View Clustering](https://mlsanity.com/paper/2607.06281/) (2026)
- [Multi-view Graph Convolutional Network with Fully Leveraging Consistency via Granular-ball-based Topology Construction, Feature Enhancement and Interactive Fusion](https://mlsanity.com/paper/2603.26729/) (2026)
- [Beyond Independence: Learning Correlated Views for Variational Incomplete Multi-View Clustering](https://mlsanity.com/paper/2608.15757/) (2026)

## Abstract (arXiv)

In this paper, we consider the problem of multi-view clustering on incomplete views. Compared with complete multi-view clustering, the view-missing problem increases the difficulty of learning common representations from different views. To address the challenge, we propose a novel incomplete multi-view clustering framework, which incorporates cross-view relation transfer and multi-view fusion learning. Specifically, based on the consistency existing in multi-view data, we devise a cross-view relation transfer-based completion module, which transfers known similar inter-instance relationships to the missing view and recovers the missing data via graph networks based on the transferred relationship graph. Then the view-specific encoders are designed to extract the recovered multi-view data, and an attention-based fusion layer is introduced to obtain the common representation. Moreover, to reduce the impact of the error caused by the inconsistency between views and obtain a better clustering structure, a joint clustering layer is introduced to optimize recovery and clustering simultaneously. Extensive experiments conducted on several real datasets demonstrate the effectiveness of the proposed method.