Sliding Window Informative Canonical Correlation Analysis
This method addresses the need for real-time correlation analysis in streaming data applications, though it appears incremental as an extension of existing CCA techniques.
The authors tackled the problem of performing canonical correlation analysis (CCA) on streaming data by proposing Sliding Window Informative CCA (SWICCA), which uses streaming PCA and a sliding window to estimate CCA components in real time, enabling scalability to high dimensions.
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.