Abstract
We have recently developed several ways of using Gaussian Processes to perform Canonical Correlation Analysis. We review several of these methods, introduce a new way to perform Canonical Correlation Analysis with Gaussian Processes which involves sphering each data stream separately with probabilistic principal component analysis (PCA), concatenating the sphered data and re-performing probabilistic PCA. We also investigate the effect of sparsifying this last method. We perform a comparative study of these methods.
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Lai, P.L., Leen, G., Fyfe, C. (2006). The Sphere-Concatenate Method for Gaussian Process Canonical Correlation Analysis. In: Kollias, S., Stafylopatis, A., Duch, W., Oja, E. (eds) Artificial Neural Networks – ICANN 2006. ICANN 2006. Lecture Notes in Computer Science, vol 4132. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11840930_31
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DOI: https://doi.org/10.1007/11840930_31
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-38871-5
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