# Multi-View Spectral Clustering via Structured Low-Rank Matrix Factorization

arXiv: https://arxiv.org/abs/1709.01212 · HTML: https://mlsanity.com/paper/1709.01212/

## Key facts

- Cited by 24 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Sep 2017.

## Built on by (most cited first, 24 of 24)

- [Self-Supervised Discriminative Feature Learning for Deep Multi-View Clustering](https://mlsanity.com/paper/2103.15069/) (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)
- [Contrastive Multi-view Subspace Clustering of Hyperspectral Images based on Graph Convolutional Networks](https://mlsanity.com/paper/2312.06068/) (2023)
- [One-step Multi-view Clustering with Diverse Representation](https://mlsanity.com/paper/2306.05437/) (2023)
- [Multi-view MERA Subspace Clustering](https://mlsanity.com/paper/2305.09095/) (2023)
- [Deep Descriptive Clustering](https://mlsanity.com/paper/2105.11549/) (2021)
- [MCoCo: Multi-level Consistency Collaborative Multi-view Clustering](https://mlsanity.com/paper/2302.13339/) (2023)
- [Rethinking Multi-view Representation Learning via Distilled Disentangling](https://mlsanity.com/paper/2403.10897/) (2024)
- [Graph-Collaborated Auto-Encoder Hashing for Multi-view Binary Clustering](https://mlsanity.com/paper/2301.02484/) (2023)
- [Disentangling Multi-view Representations Beyond Inductive Bias](https://mlsanity.com/paper/2308.01634/) (2023)
- [Anchor-based Multi-view Subspace Clustering with Hierarchical Feature Descent](https://mlsanity.com/paper/2310.07166/) (2023)
- [Interpretable Multi-View Clustering](https://mlsanity.com/paper/2405.02644/) (2024)
- [Multi-view Spectral Clustering on the Grassmannian Manifold With Hypergraph Representation](https://mlsanity.com/paper/2503.06066/) (2025)
- [Self-Learning Symmetric Multi-view Probabilistic Clustering](https://mlsanity.com/paper/2305.07307/) (2023)
- [Multiview Subspace Clustering of Hyperspectral Images based on Graph Convolutional Networks](https://mlsanity.com/paper/2403.01465/) (2024)
- [Adversarial Fair Multi-View Clustering](https://mlsanity.com/paper/2508.04071/) (2025)
- [Advanced Unsupervised Learning: A Comprehensive Overview of Multi-View Clustering Techniques](https://mlsanity.com/paper/2512.05169/) (2025)
- [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)
- [Late Fusion Multi-view Clustering via Global and Local Alignment Maximization](https://mlsanity.com/paper/2208.01198/) (2022)
- [Towards Generalized Multi-stage Clustering: Multi-view Self-distillation](https://mlsanity.com/paper/2310.18890/) (2023)
- [Balanced Multi-view Clustering](https://mlsanity.com/paper/2501.02564/) (2025)
- [Clusterability-Based Assessment of Potentially Noisy Views for Multi-View Clustering](https://mlsanity.com/paper/2604.18024/) (2026)
- [Multi-View Fair Clustering Guided by Cross-View Sensitive Information Discrepancy](https://mlsanity.com/paper/2609.25811/) (2026)

## Abstract (arXiv)

Multi-view data clustering attracts more attention than their single view counterparts due to the fact that leveraging multiple independent and complementary information from multi-view feature spaces outperforms the single one. Multi-view Spectral Clustering aims at yielding the data partition agreement over their local manifold structures by seeking eigenvalue-eigenvector decompositions. However, as we observed, such classical paradigm still suffers from (1) overlooking the flexible local manifold structure, caused by (2) enforcing the low-rank data correlation agreement among all views; worse still, (3) LRR is not intuitively flexible to capture the latent data clustering structures. In this paper, we present the structured LRR by factorizing into the latent low-dimensional data-cluster representations, which characterize the data clustering structure for each view. Upon such representation, (b) the laplacian regularizer is imposed to be capable of preserving the flexible local manifold structure for each view. (c) We present an iterative multi-view agreement strategy by minimizing the divergence objective among all factorized latent data-cluster representations during each iteration of optimization process, where such latent representation from each view serves to regulate those from other views, such intuitive process iteratively coordinates all views to be agreeable. (d) We remark that such data-cluster representation can flexibly encode the data clustering structure from any view with adaptive input cluster number. To this end, (e) a novel non-convex objective function is proposed via the efficient alternating minimization strategy. The complexity analysis are also presented. The extensive experiments conducted against the real-world multi-view datasets demonstrate the superiority over state-of-the-arts.