{"ID":2891519,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2507.17921","arxiv_id":"2507.17921","title":"Sliding Window Informative Canonical Correlation Analysis","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.","short_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...","url_abs":"https://arxiv.org/abs/2507.17921","url_pdf":"https://arxiv.org/pdf/2507.17921v2","authors":"[\"Arvind Prasadan\"]","published":"2025-07-23T20:35:15Z","proceeding":"stat.ML","tasks":"[\"stat.ML\",\"cs.LG\",\"eess.IV\",\"math.ST\",\"stat.CO\",\"stat.ME\"]","methods":"[]","has_code":false}