# Stochastic Approximation for Canonical Correlation Analysis

arXiv: https://arxiv.org/abs/1702.06818 · HTML: https://mlsanity.com/paper/1702.06818/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Feb 2017.

## Built on by (most cited first, 7 of 7)

- [An Online Riemannian PCA for Stochastic Canonical Correlation Analysis](https://mlsanity.com/paper/2106.07479/) (2021)
- [Optimization without Retraction on the Random Generalized Stiefel Manifold](https://mlsanity.com/paper/2405.01702/) (2024)
- [Normative framework for deriving neural networks with multi-compartmental neurons and non-Hebbian plasticity](https://mlsanity.com/paper/2302.10051/) (2023)
- [Nonconvex Stochastic Scaled-Gradient Descent and Generalized Eigenvector Problems](https://mlsanity.com/paper/2112.14738/) (2021)
- [The Symmetric Generalized Eigenvalue Problem as a Nash Equilibrium](https://mlsanity.com/paper/2206.04993/) (2022)
- [A Generalized EigenGame with Extensions to Multiview Representation Learning](https://mlsanity.com/paper/2211.11323/) (2022)
- [Sliding Window Informative Canonical Correlation Analysis](https://mlsanity.com/paper/2507.17921/) (2025)

## Abstract (arXiv)

We propose novel first-order stochastic approximation algorithms for canonical correlation analysis (CCA). Algorithms presented are instances of inexact matrix stochastic gradient (MSG) and inexact matrix exponentiated gradient (MEG), and achieve ε-suboptimality in the population objective in operatornamepoly(frac1ε) iterations. We also consider practical variants of the proposed algorithms and compare them with other methods for CCA both theoretically and empirically.