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Adaptive Canonical Correlation Analysis Based On Matrix Manifolds

In this paper, we formulate the Canonical Correlation Analysis (CCA) problem on matrix manifolds. This framework provides a natural way for dealing with matrix constraints and tools for building efficient algorithms even in an adaptive setting. Finally, an adaptive CCA algorithm is proposed and applied to a change detection problem in EEG signals.

preprint2012arXivOpen access
Florian YgerMaxime BerarGilles GassoAlain Rakotomamonjy
Machine Learning
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Open access4 authors1 topic
Imported metadata coverageMissing code, dataset, citation and institution fields are tracked without dominating the paper.Details
Citations: 0Reviews: 0Saves: 0Code: not linkedDataset: not linkedInstitutions: 0

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Florian YgerMaxime BerarGilles GassoAlain Rakotomamonjy

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