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.
Chapter PDF
Similar content being viewed by others
Keywords
- Data Stream
- Gaussian Process
- Canonical Correlation Analysis
- Kernel Principal Component Analysis
- Neural Network Method
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
References
Bach, F.R., Jordan, M.I.: A probabilistic interpretation of canonical correlation analysis. Technical Report 688, Dept. of Statistics, University of California (2005)
Fyfe, C., Leen, G.: Stochastic processes for canonical correlation analysis. In: 14th European Symposium on Artificial Neural Networks (2006)
Gou, Z.K., Fyfe, C.: A canonical correlation neural network for multicollinearity and functional data. Neural Networks (2003)
Lai, P.L., Fyfe, C.: A neural network implementation of canonical correlation analysis. Neural Networks 12(10), 1391–1397 (1999)
Lai, P.L., Leen, G., Fyfe, C.: A comparison of stochastic processes and artificial neural networks for canonical correlation analysis. In: International Joint Conference on Neural Networks (2006)
MacKay, D.J.C.: Introduction to gaussian processes. Technical report, University of Cambridge (1997), http://www.inference.phy.cam.uk/mackay/gpB.pdf
Mardia, K.V., Kent, J.T., Bibby, J.M.: Multivariate Analysis. Academic Press, London (1979)
Rasmussen, C.E.: Advanced Lectures on Machine Learning. In: Gaussian Processes in Machine Learning, pp. 63–71 (2003)
Scholkopf, B., Smola, A., Muller, K.-R.: Nonlinear component analysis as a kernel eigenvalue problem. Neural Computation 10, 1299–1319 (1998)
Tipping, M.: Sparse kernel principal component analysis. In: NIPS (2003)
Williams, C.K.I.: Prediction with gaussian processes: from linear regression to linear prediction and beyond. Technical report, Aston University (1997)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2006 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
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
Download citation
DOI: https://doi.org/10.1007/11840930_31
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-38871-5
Online ISBN: 978-3-540-38873-9
eBook Packages: Computer ScienceComputer Science (R0)