# Doubly Aligned Incomplete Multi-view Clustering

arXiv: https://arxiv.org/abs/1903.02785 · HTML: https://mlsanity.com/paper/1903.02785/

## Key facts

- Cited by 40 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Mar 2019.

## Built on by (most cited first, 30 of 40)

- [Multimodal Fusion on Low-quality Data: A Comprehensive Survey](https://mlsanity.com/paper/2404.18947/) (2024)
- [Deep Incomplete Multi-view Clustering with Cross-view Partial Sample and Prototype Alignment](https://mlsanity.com/paper/2303.15689/) (2023)
- [A Survey on Incomplete Multi-view Clustering](https://mlsanity.com/paper/2208.08040/) (2022)
- [Uncertainty-Aware Multi-View Representation Learning](https://mlsanity.com/paper/2201.05776/) (2022)
- [Decoupled Contrastive Multi-View Clustering with High-Order Random Walks](https://mlsanity.com/paper/2308.11164/) (2023)
- [Incomplete Multi-view Clustering via Prototype-based Imputation](https://mlsanity.com/paper/2301.11045/) (2023)
- [Fast Continual Multi-View Clustering with Incomplete Views](https://mlsanity.com/paper/2306.02389/) (2023)
- [Localized Sparse Incomplete Multi-view Clustering](https://mlsanity.com/paper/2208.02998/) (2022)
- [Information Recovery-Driven Deep Incomplete Multiview Clustering Network](https://mlsanity.com/paper/2304.00429/) (2023)
- [Incomplete Contrastive Multi-View Clustering with High-Confidence Guiding](https://mlsanity.com/paper/2312.08697/) (2023)
- [RedCore: Relative Advantage Aware Cross-modal Representation Learning for Missing Modalities with Imbalanced Missing Rates](https://mlsanity.com/paper/2312.10386/) (2023)
- [DICNet: Deep Instance-Level Contrastive Network for Double Incomplete Multi-View Multi-Label Classification](https://mlsanity.com/paper/2303.08358/) (2023)
- [Semantic Invariant Multi-view Clustering with Fully Incomplete Information](https://mlsanity.com/paper/2305.12743/) (2023)
- [Incomplete Multi-view Clustering via Cross-view Relation Transfer](https://mlsanity.com/paper/2112.00739/) (2021)
- [Self-supervised Image Clustering from Multiple Incomplete Views via Constrastive Complementary Generation](https://mlsanity.com/paper/2209.11927/) (2022)
- [CDIMC-net: Cognitive Deep Incomplete Multi-view Clustering Network](https://mlsanity.com/paper/2403.19514/) (2024)
- [Scalable Incomplete Multi-View Clustering with Structure Alignment](https://mlsanity.com/paper/2308.16541/) (2023)
- [Deep Descriptive Clustering](https://mlsanity.com/paper/2105.11549/) (2021)
- [Joint Projection Learning and Tensor Decomposition Based Incomplete Multi-view Clustering](https://mlsanity.com/paper/2310.04038/) (2023)
- [Manifold-based Incomplete Multi-view Clustering via Bi-Consistency Guidance](https://mlsanity.com/paper/2405.10987/) (2024)
- [MORI-RAN: Multi-view Robust Representation Learning via Hybrid Contrastive Fusion](https://mlsanity.com/paper/2208.12545/) (2022)
- [Few-shot Partial Multi-view Learning](https://mlsanity.com/paper/2105.02046/) (2021)
- [Global Graph Propagation with Hierarchical Information Transfer for Incomplete Contrastive Multi-view Clustering](https://mlsanity.com/paper/2502.19291/) (2025)
- [ACTIVE:Augmentation-Free Graph Contrastive Learning for Partial Multi-View Clustering](https://mlsanity.com/paper/2203.00186/) (2022)
- [Explicit View-labels Matter: A Multifacet Complementarity Study of Multi-view Clustering](https://mlsanity.com/paper/2205.02507/) (2022)
- [Unpaired Multi-view Clustering via Reliable View Guidance](https://mlsanity.com/paper/2404.17894/) (2024)
- [Multi-level Reliable Guidance for Unpaired Multi-view Clustering](https://mlsanity.com/paper/2407.01247/) (2024)
- [Balancing Complementarity and Consistency via Delayed Activation in Incomplete Multi-view Clustering](https://mlsanity.com/paper/2407.17744/) (2024)
- [PatentNet: A Large-Scale Incomplete Multiview, Multimodal, Multilabel Industrial Goods Image Database](https://mlsanity.com/paper/2106.12139/) (2021)
- [TSK Fuzzy System Towards Few Labeled Incomplete Multi-View Data Classification](https://mlsanity.com/paper/2110.05610/) (2021)

## Abstract (arXiv)

Nowadays, multi-view clustering has attracted more and more attention. To date, almost all the previous studies assume that views are complete. However, in reality, it is often the case that each view may contain some missing instances. Such incompleteness makes it impossible to directly use traditional multi-view clustering methods. In this paper, we propose a Doubly Aligned Incomplete Multi-view Clustering algorithm (DAIMC) based on weighted semi-nonnegative matrix factorization (semi-NMF). Specifically, on the one hand, DAIMC utilizes the given instance alignment information to learn a common latent feature matrix for all the views. On the other hand, DAIMC establishes a consensus basis matrix with the help of L2,1-Norm regularized regression for reducing the influence of missing instances. Consequently, compared with existing methods, besides inheriting the strength of semi-NMF with ability to handle negative entries, DAIMC has two unique advantages: 1) solving the incomplete view problem by introducing a respective weight matrix for each view, making it able to easily adapt to the case with more than two views; 2) reducing the influence of view incompleteness on clustering by enforcing the basis matrices of individual views being aligned with the help of regression. Experiments on four real-world datasets demonstrate its advantages.