# ORCCA: Optimal Randomized Canonical Correlation Analysis

arXiv: https://arxiv.org/abs/1910.05384 · HTML: https://mlsanity.com/paper/1910.05384/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Oct 2019.

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

- [TAP: The Attention Patch for Cross-Modal Knowledge Transfer from Unlabeled Modality](https://mlsanity.com/paper/2302.02224/) (2023)

## Abstract (arXiv)

Random features approach has been widely used for kernel approximation in large-scale machine learning. A number of recent studies have explored data-dependent sampling of features, modifying the stochastic oracle from which random features are sampled. While proposed techniques in this realm improve the approximation, their suitability is often verified on a single learning task. In this paper, we propose a task-specific scoring rule for selecting random features, which can be employed for different applications with some adjustments. We restrict our attention to Canonical Correlation Analysis (CCA), and we provide a novel, principled guide for finding the score function maximizing the canonical correlations. We prove that this method, called ORCCA, can outperform (in expectation) the corresponding Kernel CCA with a default kernel. Numerical experiments verify that ORCCA is significantly superior than other approximation techniques in the CCA task.