# Manifold-based Incomplete Multi-view Clustering via Bi-Consistency Guidance

arXiv: https://arxiv.org/abs/2405.10987 · HTML: https://mlsanity.com/paper/2405.10987/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 59 references that are in the map.
- First posted on arXiv 16 May 2024.

## Builds on (most cited first, 15 of 15)

- [Survey on Deep Multi-modal Data Analytics: Collaboration, Rivalry and Fusion](https://mlsanity.com/paper/2006.08159/) (2020)
- [Doubly Aligned Incomplete Multi-view Clustering](https://mlsanity.com/paper/1903.02785/) (2019)
- [Structure Matters: Tackling the Semantic Discrepancy in Diffusion Models for Image Inpainting](https://mlsanity.com/paper/2403.19898/) (2024)
- [VIGAN: Missing View Imputation with Generative Adversarial Networks](https://mlsanity.com/paper/1708.06724/) (2017)
- [Essential Tensor Learning for Multi-view Spectral Clustering](https://mlsanity.com/paper/1807.03602/) (2018)
- [A Survey on Incomplete Multi-view Clustering](https://mlsanity.com/paper/2208.08040/) (2022)
- [Switchable Online Knowledge Distillation](https://mlsanity.com/paper/2209.04996/) (2022)
- [Adaptive Data-Free Quantization](https://mlsanity.com/paper/2303.06869/) (2023)
- [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)
- [Iterative Views Agreement: An Iterative Low-Rank based Structured Optimization Method to Multi-View Spectral Clustering](https://mlsanity.com/paper/1608.05560/) (2016)
- [Kernelized Multiview Subspace Analysis by Self-weighted Learning](https://mlsanity.com/paper/1911.10357/) (2019)
- [Localized Sparse Incomplete Multi-view Clustering](https://mlsanity.com/paper/2208.02998/) (2022)
- [Rethinking Data-Free Quantization as a Zero-Sum Game](https://mlsanity.com/paper/2302.09572/) (2023)
- [Graph-Collaborated Auto-Encoder Hashing for Multi-view Binary Clustering](https://mlsanity.com/paper/2301.02484/) (2023)

## Built on by (most cited first, 5 of 5)

- [SCSA: Exploring the Synergistic Effects Between Spatial and Channel Attention](https://mlsanity.com/paper/2407.05128/) (2024)
- [Unsupervised Domain Adaptive Person Search via Dual Self-Calibration](https://mlsanity.com/paper/2412.16506/) (2024)
- [Physics-Informed Geometric Operators to Support Surrogate, Dimension Reduction and Generative Models for Engineering Design](https://mlsanity.com/paper/2407.07611/) (2024)
- [Anchor Learning with Potential Cluster Constraints for Multi-view Clustering](https://mlsanity.com/paper/2412.16519/) (2024)
- [Halo Separation-guided Underwater Multi-scale Image Restoration](https://mlsanity.com/paper/2605.10374/) (2026)

## Abstract (arXiv)

Incomplete multi-view clustering primarily focuses on dividing unlabeled data into corresponding categories with missing instances, and has received intensive attention due to its superiority in real applications. Considering the influence of incomplete data, the existing methods mostly attempt to recover data by adding extra terms. However, for the unsupervised methods, a simple recovery strategy will cause errors and outlying value accumulations, which will affect the performance of the methods. Broadly, the previous methods have not taken the effectiveness of recovered instances into consideration, or cannot flexibly balance the discrepancies between recovered data and original data. To address these problems, we propose a novel method termed Manifold-based Incomplete Multi-view clustering via Bi-consistency guidance (MIMB), which flexibly recovers incomplete data among various views, and attempts to achieve biconsistency guidance via reverse regularization. In particular, MIMB adds reconstruction terms to representation learning by recovering missing instances, which dynamically examines the latent consensus representation. Moreover, to preserve the consistency information among multiple views, MIMB implements a biconsistency guidance strategy with reverse regularization of the consensus representation and proposes a manifold embedding measure for exploring the hidden structure of the recovered data. Notably, MIMB aims to balance the importance of different views, and introduces an adaptive weight term for each view. Finally, an optimization algorithm with an alternating iteration optimization strategy is designed for final clustering. Extensive experimental results on 6 benchmark datasets are provided to confirm that MIMB can significantly obtain superior results as compared with several state-of-the-art baselines.