# Sliding Window Informative Canonical Correlation Analysis

arXiv: https://arxiv.org/abs/2507.17921 · HTML: https://mlsanity.com/paper/2507.17921/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 34 references that are in the map.
- First posted on arXiv 23 Jul 2025.

## Builds on (most cited first, 6 of 6)

- [Streaming PCA and Subspace Tracking: The Missing Data Case](https://mlsanity.com/paper/1806.04609/) (2018)
- [PKU-DyMVHumans: A Multi-View Video Benchmark for High-Fidelity Dynamic Human Modeling](https://mlsanity.com/paper/2403.16080/) (2024)
- [Stochastic Canonical Correlation Analysis](https://mlsanity.com/paper/1702.06533/) (2017)
- [Stochastic Approximation for Canonical Correlation Analysis](https://mlsanity.com/paper/1702.06818/) (2017)
- [Contrastive Principal Component Analysis](https://mlsanity.com/paper/1709.06716/) (2017)
- [An Online Riemannian PCA for Stochastic Canonical Correlation Analysis](https://mlsanity.com/paper/2106.07479/) (2021)

## Abstract (arXiv)

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.