# Canonical Correlation Analysis in high dimensions with structured regularization

arXiv: https://arxiv.org/abs/2011.01650 · HTML: https://mlsanity.com/paper/2011.01650/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Nov 2020.

## Built on by (most cited first, 4 of 4)

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- [Automated Gradient-Driven Parameter Sharing for Low-Resource Multilingual Speech-to-Text Translation](https://mlsanity.com/paper/2603.25836/) (2026)

## Abstract (arXiv)

Canonical correlation analysis (CCA) is a technique for measuring the association between two multivariate data matrices. A regularized modification of canonical correlation analysis (RCCA) which imposes an ℓ2 penalty on the CCA coefficients is widely used in applications with high-dimensional data. One limitation of such regularization is that it ignores any data structure, treating all the features equally, which can be ill-suited for some applications. In this paper we introduce several approaches to regularizing CCA that take the underlying data structure into account. In particular, the proposed group regularized canonical correlation analysis (GRCCA) is useful when the variables are correlated in groups. We illustrate some computational strategies to avoid excessive computations with regularized CCA in high dimensions. We demonstrate the application of these methods in our motivating application from neuroscience, as well as in a small simulation example.