AI RESEARCH
Sliding Window Informative Canonical Correlation Analysis
arXiv CS.LG
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ArXi:2507.17921v2 Announce Type: replace-cross 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.