Sliding Window Informative Canonical Correlation Analysis
Abstract
Canonical correlation analysis (CCA) is a technique for finding correlated sets of features between two datasets. In this paper, we propose a novel extension of CCA to the online, streaming data setting: Sliding Window Informative Canonical Correlation Analysis (SWICCA). Our method uses a streaming principal component analysis (PCA) algorithm as a backend and uses these outputs combined with a small sliding window of samples to estimate the CCA components in real time. We motivate and describe our algorithm, provide numerical simulations to characterize its performance, and provide a theoretical performance guarantee. The SWICCA method is applicable and scalable to extremely high dimensions, and we provide a real-data example that demonstrates this capability.
Keywords
Cite
@article{arxiv.2507.17921,
title = {Sliding Window Informative Canonical Correlation Analysis},
author = {Arvind Prasadan},
journal= {arXiv preprint arXiv:2507.17921},
year = {2026}
}
Comments
11 pages (double column), submitted; revised with updated simulations