# Towards Aligned Canonical Correlation Analysis: Preliminary Formulation and Proof-of-Concept Results

arXiv: https://arxiv.org/abs/2312.00296 · HTML: https://mlsanity.com/paper/2312.00296/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 11 references that are in the map.
- First posted on arXiv 1 Dec 2023.

## Builds on (most cited first, 5 of 5)

- [Barlow Twins: Self-Supervised Learning via Redundancy Reduction](https://mlsanity.com/paper/2103.03230/) (2021)
- [A Cookbook of Self-Supervised Learning](https://mlsanity.com/paper/2304.12210/) (2023)
- [From Canonical Correlation Analysis to Self-supervised Graph Neural Networks](https://mlsanity.com/paper/2106.12484/) (2021)
- [Graph Barlow Twins: A self-supervised representation learning framework for graphs](https://mlsanity.com/paper/2106.02466/) (2021)
- [Graph Multiview Canonical Correlation Analysis](https://mlsanity.com/paper/1811.12345/) (2018)

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

- [Unpaired Canonical Correlation Analysis](https://mlsanity.com/paper/2610.09530/) (2026)

## Abstract (arXiv)

Canonical Correlation Analysis (CCA) has been widely applied to jointly embed multiple views of data in a maximally correlated latent space. However, the alignment between various data perspectives, which is required by traditional approaches, is unclear in many practical cases. In this work we propose a new framework Aligned Canonical Correlation Analysis (ACCA), to address this challenge by iteratively solving the alignment and multi-view embedding.