# Advanced Unsupervised Learning: A Comprehensive Overview of Multi-View Clustering Techniques

arXiv: https://arxiv.org/abs/2512.05169 · HTML: https://mlsanity.com/paper/2512.05169/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 25 of its 154 references that are in the map.
- First posted on arXiv 4 Dec 2025.

## Builds on (most cited first, 25 of 25)

- [A Comprehensive Survey on Graph Neural Networks](https://mlsanity.com/paper/1901.00596/) (2019)
- [Return of Frustratingly Easy Domain Adaptation](https://mlsanity.com/paper/1511.05547/) (2015)
- [Session-based Recommendation with Graph Neural Networks](https://mlsanity.com/paper/1811.00855/) (2018)
- [A Survey on Multi-view Learning](https://mlsanity.com/paper/1304.5634/) (2013)
- [Heterogeneous Graph Neural Networks for Malicious Account Detection](https://mlsanity.com/paper/2002.12307/) (2020)
- [Deep Generalized Canonical Correlation Analysis](https://mlsanity.com/paper/1702.02519/) (2017)
- [A Survey on Incomplete Multi-view Clustering](https://mlsanity.com/paper/2208.08040/) (2022)
- [Structured Graph Learning for Scalable Subspace Clustering: From Single-view to Multi-view](https://mlsanity.com/paper/2102.07943/) (2021)
- [Multi-graph Fusion for Multi-view Spectral Clustering](https://mlsanity.com/paper/1909.06940/) (2019)
- [Lifting Multi-View Detection and Tracking to the Bird's Eye View](https://mlsanity.com/paper/2403.12573/) (2024)
- [Multi-View Spectral Clustering via Structured Low-Rank Matrix Factorization](https://mlsanity.com/paper/1709.01212/) (2017)
- [Spectral Perturbation Meets Incomplete Multi-view Data](https://mlsanity.com/paper/1906.00098/) (2019)
- [Convex Sparse Spectral Clustering: Single-view to Multi-view](https://mlsanity.com/paper/1511.06860/) (2015)
- [Clustering on Multi-Layer Graphs via Subspace Analysis on Grassmann Manifolds](https://mlsanity.com/paper/1303.2221/) (2013)
- [Multi-View Spectral Clustering with High-Order Optimal Neighborhood Laplacian Matrix](https://mlsanity.com/paper/2008.13539/) (2020)
- [Deep matrix factorizations](https://mlsanity.com/paper/2010.00380/) (2020)
- [Unsupervised Multi-view Clustering by Squeezing Hybrid Knowledge from Cross View and Each View](https://mlsanity.com/paper/2008.09990/) (2020)
- [High-order Multi-view Clustering for Generic Data](https://mlsanity.com/paper/2209.10838/) (2022)
- [Parea: multi-view ensemble clustering for cancer subtype discovery](https://mlsanity.com/paper/2209.15399/) (2022)
- [Interpretable Deep Learning Methods for Multiview Learning](https://mlsanity.com/paper/2302.07930/) (2023)
- [Smoothed Multi-View Subspace Clustering](https://mlsanity.com/paper/2106.09875/) (2021)
- [Multi-view Generative Adversarial Networks](https://mlsanity.com/paper/1611.02019/) (2016)
- [A Dynamic Mode Decomposition Approach for Decentralized Spectral Clustering of Graphs](https://mlsanity.com/paper/2203.00004/) (2022)
- [Multi-view Orthonormalized Partial Least Squares: Regularizations and Deep Extensions](https://mlsanity.com/paper/2007.05028/) (2020)
- [Consistency-aware and Inconsistency-aware Graph-based Multi-view Clustering](https://mlsanity.com/paper/2011.12532/) (2020)

## Built on by (most cited first, 1 of 1)

- [Simple Yet Effective Selective Imputation for Incomplete Multi-view Clustering](https://mlsanity.com/paper/2512.10327/) (2025)

## Abstract (arXiv)

Machine learning techniques face numerous challenges to achieve optimal performance. These include computational constraints, the limitations of single-view learning algorithms and the complexity of processing large datasets from different domains, sources or views. In this context, multi-view clustering (MVC), a class of unsupervised multi-view learning, emerges as a powerful approach to overcome these challenges. MVC compensates for the shortcomings of single-view methods and provides a richer data representation and effective solutions for a variety of unsupervised learning tasks. In contrast to traditional single-view approaches, the semantically rich nature of multi-view data increases its practical utility despite its inherent complexity. This survey makes a threefold contribution: (1) a systematic categorization of multi-view clustering methods into well-defined groups, including co-training, co-regularization, subspace, deep learning, kernel-based, anchor-based, and graph-based strategies; (2) an in-depth analysis of their respective strengths, weaknesses, and practical challenges, such as scalability and incomplete data; and (3) a forward-looking discussion of emerging trends, interdisciplinary applications, and future directions in MVC research. This study represents an extensive workload, encompassing the review of over 140 foundational and recent publications, the development of comparative insights on integration strategies such as early fusion, late fusion, and joint learning, and the structured investigation of practical use cases in the areas of healthcare, multimedia, and social network analysis. By integrating these efforts, this work aims to fill existing gaps in MVC research and provide actionable insights for the advancement of the field.