Adaptive Canonical Correlation Analysis Based On Matrix Manifolds
arXiv:1206.6453v148 citations
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This work addresses matrix constraint handling in CCA for domain-specific applications like EEG analysis, but it appears incremental as it adapts existing manifold methods to CCA.
The paper tackled the problem of performing Canonical Correlation Analysis (CCA) under matrix constraints by formulating it on matrix manifolds, resulting in an adaptive algorithm applied to EEG signal change detection.
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.