# CDIMC-net: Cognitive Deep Incomplete Multi-view Clustering Network

arXiv: https://arxiv.org/abs/2403.19514 · HTML: https://mlsanity.com/paper/2403.19514/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 36 references that are in the map.
- First posted on arXiv 28 Mar 2024.

## Builds on (most cited first, 6 of 6)

- [Unsupervised Deep Embedding for Clustering Analysis](https://mlsanity.com/paper/1511.06335/) (2015)
- [Towards K-means-friendly Spaces: Simultaneous Deep Learning and Clustering](https://mlsanity.com/paper/1610.04794/) (2016)
- [Doubly Aligned Incomplete Multi-view Clustering](https://mlsanity.com/paper/1903.02785/) (2019)
- [One-Pass Incomplete Multi-view Clustering](https://mlsanity.com/paper/1903.00637/) (2019)
- [Spectral Perturbation Meets Incomplete Multi-view Data](https://mlsanity.com/paper/1906.00098/) (2019)
- [Incomplete Multi-view Clustering via Graph Regularized Matrix Factorization](https://mlsanity.com/paper/1809.05998/) (2018)

## Built on by (most cited first, 8 of 8)

- [Multimodal Fusion on Low-quality Data: A Comprehensive Survey](https://mlsanity.com/paper/2404.18947/) (2024)
- [Federated Incomplete Multi-View Clustering with Heterogeneous Graph Neural Networks](https://mlsanity.com/paper/2406.08524/) (2024)
- [Federated Incomplete Multi-view Clustering with Globally Fused Graph Guidance](https://mlsanity.com/paper/2506.15703/) (2025)
- [Multi-view Clustering via Bi-level Decoupling and Consistency Learning](https://mlsanity.com/paper/2508.13499/) (2025)
- [MCFCN: Multi-View Clustering via a Fusion-Consensus Graph Convolutional Network](https://mlsanity.com/paper/2511.05554/) (2025)
- [Missing Pattern Tree based Decision Grouping and Ensemble for Enhancing Pair Utilization in Deep Incomplete Multi-View Clustering](https://mlsanity.com/paper/2512.21510/) (2025)
- [Beyond Missing Rates: Rethinking Incomplete Multi-View Clustering with Protocol Divergence](https://mlsanity.com/paper/2606.04857/) (2026)
- [Beyond Independence: Learning Correlated Views for Variational Incomplete Multi-View Clustering](https://mlsanity.com/paper/2608.15757/) (2026)

## Abstract (arXiv)

In recent years, incomplete multi-view clustering, which studies the challenging multi-view clustering problem on missing views, has received growing research interests. Although a series of methods have been proposed to address this issue, the following problems still exist: 1) Almost all of the existing methods are based on shallow models, which is difficult to obtain discriminative common representations. 2) These methods are generally sensitive to noise or outliers since the negative samples are treated equally as the important samples. In this paper, we propose a novel incomplete multi-view clustering network, called Cognitive Deep Incomplete Multi-view Clustering Network (CDIMC-net), to address these issues. Specifically, it captures the high-level features and local structure of each view by incorporating the view-specific deep encoders and graph embedding strategy into a framework. Moreover, based on the human cognition, i.e., learning from easy to hard, it introduces a self-paced strategy to select the most confident samples for model training, which can reduce the negative influence of outliers. Experimental results on several incomplete datasets show that CDIMC-net outperforms the state-of-the-art incomplete multi-view clustering methods.