# Tensor-based Multi-view Spectral Clustering via Shared Latent Space

arXiv: https://arxiv.org/abs/2207.11559 · HTML: https://mlsanity.com/paper/2207.11559/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 76 references that are in the map.
- First posted on arXiv 23 Jul 2022.

## Builds on (most cited first, 10 of 10)

- [Adjusting for Chance Clustering Comparison Measures](https://mlsanity.com/paper/1512.01286/) (2015)
- [On Unifying Multi-View Self-Representations for Clustering by Tensor Multi-Rank Minimization](https://mlsanity.com/paper/1610.07126/) (2016)
- [Multi-View Spectral Clustering Tailored Tensor Low-Rank Representation](https://mlsanity.com/paper/2004.14705/) (2020)
- [Disentangled Representation Learning and Generation with Manifold Optimization](https://mlsanity.com/paper/2006.07046/) (2020)
- [Tensor Canonical Correlation Analysis for Multi-view Dimension Reduction](https://mlsanity.com/paper/1502.02330/) (2015)
- [Unsupervised Energy-based Out-of-distribution Detection using Stiefel-Restricted Kernel Machine](https://mlsanity.com/paper/2102.08443/) (2021)
- [Unsupervised learning of disentangled representations in deep restricted kernel machines with orthogonality constraints](https://mlsanity.com/paper/2011.12659/) (2020)
- [Multi-view Subspace Clustering via Partition Fusion](https://mlsanity.com/paper/1912.01201/) (2019)
- [Generative Restricted Kernel Machines: A Framework for Multi-view Generation and Disentangled Feature Learning](https://mlsanity.com/paper/1906.08144/) (2019)
- [Robust Generative Restricted Kernel Machines using Weighted Conjugate Feature Duality](https://mlsanity.com/paper/2002.01180/) (2020)

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

- [Unsupervised Neighborhood Propagation Kernel Layers for Semi-supervised Node Classification](https://mlsanity.com/paper/2301.13764/) (2023)
- [Randomized based restricted kernel machine for hyperspectral image classification](https://mlsanity.com/paper/2503.05837/) (2025)
- [Low-Rank Multitask Learning based on Tensorized SVMs and LSSVMs](https://mlsanity.com/paper/2308.16056/) (2023)
- [Generalizable and Robust Spectral Method for Multi-view Representation Learning](https://mlsanity.com/paper/2411.02138/) (2024)
- [MaD-Mix: Multi-Modal Data Mixtures via Latent Space Coupling for Vision-Language Model Training](https://mlsanity.com/paper/2602.07790/) (2026)

## Abstract (arXiv)

Multi-view Spectral Clustering (MvSC) attracts increasing attention due to diverse data sources. However, most existing works are prohibited in out-of-sample predictions and overlook model interpretability and exploration of clustering results. In this paper, a new method for MvSC is proposed via a shared latent space from the Restricted Kernel Machine framework. Through the lens of conjugate feature duality, we cast the weighted kernel principal component analysis problem for MvSC and develop a modified weighted conjugate feature duality to formulate dual variables. In our method, the dual variables, playing the role of hidden features, are shared by all views to construct a common latent space, coupling the views by learning projections from view-specific spaces. Such single latent space promotes well-separated clusters and provides straightforward data exploration, facilitating visualization and interpretation. Our method requires only a single eigendecomposition, whose dimension is independent of the number of views. To boost higher-order correlations, tensor-based modelling is introduced without increasing computational complexity. Our method can be flexibly applied with out-of-sample extensions, enabling greatly improved efficiency for large-scale data with fixed-size kernel schemes. Numerical experiments verify that our method is effective regarding accuracy, efficiency, and interpretability, showing a sharp eigenvalue decay and distinct latent variable distributions.