# Iterative Views Agreement: An Iterative Low-Rank based Structured Optimization Method to Multi-View Spectral Clustering

arXiv: https://arxiv.org/abs/1608.05560 · HTML: https://mlsanity.com/paper/1608.05560/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Aug 2016.

## Built on by (most cited first, 18 of 18)

- [Self-Supervised Discriminative Feature Learning for Deep Multi-View Clustering](https://mlsanity.com/paper/2103.15069/) (2021)
- [Structured Graph Learning for Scalable Subspace Clustering: From Single-view to Multi-view](https://mlsanity.com/paper/2102.07943/) (2021)
- [Investigating and Mitigating the Side Effects of Noisy Views for Self-Supervised Clustering Algorithms in Practical Multi-View Scenarios](https://mlsanity.com/paper/2303.17245/) (2023)
- [Seeking Commonness and Inconsistencies: A Jointly Smoothed Approach to Multi-view Subspace Clustering](https://mlsanity.com/paper/2203.08060/) (2022)
- [A Comprehensive Survey on Spectral Clustering with Graph Structure Learning](https://mlsanity.com/paper/2501.13597/) (2025)
- [Combining pretrained CNN feature extractors to enhance clustering of complex natural images](https://mlsanity.com/paper/2101.02767/) (2021)
- [Multi-view MERA Subspace Clustering](https://mlsanity.com/paper/2305.09095/) (2023)
- [Fine-grained Cross-modal Fusion based Refinement for Text-to-Image Synthesis](https://mlsanity.com/paper/2302.08706/) (2023)
- [Federated Deep Multi-View Clustering with Global Self-Supervision](https://mlsanity.com/paper/2309.13697/) (2023)
- [Manifold-based Incomplete Multi-view Clustering via Bi-Consistency Guidance](https://mlsanity.com/paper/2405.10987/) (2024)
- [Graph-Collaborated Auto-Encoder Hashing for Multi-view Binary Clustering](https://mlsanity.com/paper/2301.02484/) (2023)
- [Smoothed Multi-View Subspace Clustering](https://mlsanity.com/paper/2106.09875/) (2021)
- [Deep Adversarial Inconsistent Cognitive Sampling for Multi-view Progressive Subspace Clustering](https://mlsanity.com/paper/2101.03783/) (2021)
- [Multi-view Clustering with Deep Matrix Factorization and Global Graph Refinement](https://mlsanity.com/paper/2105.00248/) (2021)
- [Deep Cost-sensitive Learning for Wheat Frost Detection](https://mlsanity.com/paper/2212.12856/) (2022)
- [Domain Adaptive Person Search via GAN-based Scene Synthesis for Cross-scene Videos](https://mlsanity.com/paper/2308.04322/) (2023)
- [Scalable Multi-view Clustering via Explicit Kernel Features Maps](https://mlsanity.com/paper/2402.04794/) (2024)
- [Anchor Learning with Potential Cluster Constraints for Multi-view Clustering](https://mlsanity.com/paper/2412.16519/) (2024)

## Abstract (arXiv)

Multi-view spectral clustering, which aims at yielding an agreement or consensus data objects grouping across multi-views with their graph laplacian matrices, is a fundamental clustering problem. Among the existing methods, Low-Rank Representation (LRR) based method is quite superior in terms of its effectiveness, intuitiveness and robustness to noise corruptions. However, it aggressively tries to learn a common low-dimensional subspace for multi-view data, while inattentively ignoring the local manifold structure in each view, which is critically important to the spectral clustering; worse still, the low-rank minimization is enforced to achieve the data correlation consensus among all views, failing to flexibly preserve the local manifold structure for each view. In this paper, 1) we propose a multi-graph laplacian regularized LRR with each graph laplacian corresponding to one view to characterize its local manifold structure. 2) Instead of directly enforcing the low-rank minimization among all views for correlation consensus, we separately impose low-rank constraint on each view, coupled with a mutual structural consensus constraint, where it is able to not only well preserve the local manifold structure but also serve as a constraint for that from other views, which iteratively makes the views more agreeable. Extensive experiments on real-world multi-view data sets demonstrate its superiority.