Sliding Window Informative Canonical Correlation Analysis
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arXiv
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866909027458351104 |
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| author | Prasadan, Arvind |
| author_facet | Prasadan, Arvind |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_17921 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Sliding Window Informative Canonical Correlation Analysis Prasadan, Arvind Machine Learning Image and Video Processing Statistics Theory Computation Methodology 62H20, 62H25 (Primary) 62J10, 62L10 (Secondary) 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. |
| title | Sliding Window Informative Canonical Correlation Analysis |
| topic | Machine Learning Image and Video Processing Statistics Theory Computation Methodology 62H20, 62H25 (Primary) 62J10, 62L10 (Secondary) |
| url | https://arxiv.org/abs/2507.17921 |