# Graph Multiview Canonical Correlation Analysis

arXiv: https://arxiv.org/abs/1811.12345 · HTML: https://mlsanity.com/paper/1811.12345/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Nov 2018.

## Built on by (most cited first, 10 of 10)

- [Semantically Consistent Multi-view Representation Learning](https://mlsanity.com/paper/2303.04366/) (2023)
- [Low-Rank Adaptation Redux for Large Models](https://mlsanity.com/paper/2604.21905/) (2026)
- [Sparse Partial Least Squares for Coarse Noisy Graph Alignment](https://mlsanity.com/paper/2104.02810/) (2021)
- [Towards Aligned Canonical Correlation Analysis: Preliminary Formulation and Proof-of-Concept Results](https://mlsanity.com/paper/2312.00296/) (2023)
- [Graph Neural Networks for Parkinsons Disease Detection](https://mlsanity.com/paper/2409.07884/) (2024)
- [Multiview Canonical Correlation Analysis for Automatic Pathological Speech Detection](https://mlsanity.com/paper/2409.17276/) (2024)
- [Adaptive incomplete multi-view learning via tensor graph completion](https://mlsanity.com/paper/2208.03710/) (2022)
- [Multi-view Feature Extraction based on Dual Contrastive Head](https://mlsanity.com/paper/2302.03932/) (2023)
- [Multi-view Feature Extraction based on Triple Contrastive Heads](https://mlsanity.com/paper/2303.12615/) (2023)
- [Data eccentricity, asymptotics of Gaussian RBF reproducing kernel Hilbert space, and kernel PCA](https://mlsanity.com/paper/2607.21823/) (2026)

## Abstract (arXiv)

Multiview canonical correlation analysis (MCCA) seeks latent low-dimensional representations encountered with multiview data of shared entities (a.k.a. common sources). However, existing MCCA approaches do not exploit the geometry of the common sources, which may be available a priori, or can be constructed using certain domain knowledge. This prior information about the common sources can be encoded by a graph, and be invoked as a regularizer to enrich the maximum variance MCCA framework. In this context, the present paper's novel graph-regularized (G) MCCA approach minimizes the distance between the wanted canonical variables and the common low-dimensional representations, while accounting for graph-induced knowledge of the common sources. Relying on a function capturing the extent low-dimensional representations of the multiple views are similar, a generalization bound of GMCCA is established based on Rademacher's complexity. Tailored for setups where the number of data pairs is smaller than the data vector dimensions, a graph-regularized dual MCCA approach is also developed. To further deal with nonlinearities present in the data, graph-regularized kernel MCCA variants are put forward too. Interestingly, solutions of the graph-regularized linear, dual, and kernel MCCA, are all provided in terms of generalized eigenvalue decomposition. Several corroborating numerical tests using real datasets are provided to showcase the merits of the graph-regularized MCCA variants relative to several competing alternatives including MCCA, Laplacian-regularized MCCA, and (graph-regularized) PCA.