Sliding Window Informative Canonical Correlation Analysis

• URL: http://arxiv.org/abs/2507.17921v1
• Date: Wed, 23 Jul 2025 20:35:15 GMT
• Title: Sliding Window Informative Canonical Correlation Analysis
• Authors: Arvind Prasadan,
• Abstract summary: Canonical correlation analysis (CCA) is a technique for finding correlated sets of features between two datasets.<n>We propose a novel extension of CCA to the online, streaming data setting: Sliding Window Informative Canonical Correlation Analysis (SWICCA)
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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