Sliding Window Informative Canonical Correlation Analysis

TL;DR

This paper introduces SWICCA, an online extension of canonical correlation analysis that leverages streaming PCA and sliding windows for real-time, scalable feature correlation estimation in high-dimensional data.

Contribution

The paper presents a novel online CCA method, SWICCA, combining streaming PCA and sliding windows, with theoretical guarantees and high-dimensional scalability.

Findings

SWICCA performs well in numerical simulations.

The method provides theoretical performance guarantees.

SWICCA is scalable to extremely high-dimensional data.

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.