Abstract
We have recently developed several ways of performing Canonical Correlation Analysis [1,5,7,4] with probabilistic methods rather than the standard statistical tools. However, the computational demands of training such methods scales with the square of the number of samples, making these methods uncompetitive with e.g. artificial neural network methods [3,2]. In this paper, we examine two recent developments which sparsify probabilistic methods of performing canonical correlation analysis.
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References
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., Fyfe, C.: A latent variable implementation of canonical correlation analysis for data visualisation. In: International Joint Conference on Neural Networks (2006)
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)
Lawrence, N.: Probabilistic non-linear principal component analysis with Gaussian process latent variable models. Journal of Machine Learning Research 6, 1783–1816 (2005)
Leen, G., Fyfe, C.: A Gaussian process latent variable model formulation of canonical correlation analysis. In: 14th European Symposium on Artificial Neural Networks (2006)
Mardia, K.V., Kent, J.T., Bibby, J.M.: Multivariate Analysis. Academic Press, London (1979)
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)
Tipping, M.E., Bishop, C.M.: Probabilistic principal component analysis. Journal of the Royal Statistical Society, B 6(3), 611–622 (1999)
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Fyfe, C., Leen, G. (2006). Two Methods for Sparsifying Probabilistic Canonical Correlation Analysis. In: King, I., Wang, J., Chan, LW., Wang, D. (eds) Neural Information Processing. ICONIP 2006. Lecture Notes in Computer Science, vol 4232. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11893028_41
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DOI: https://doi.org/10.1007/11893028_41
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-46479-2
Online ISBN: 978-3-540-46480-8
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