论文信息 - Kernel and Nonlinear Canonical Correlation Analysis

Kernel and Nonlinear Canonical Correlation Analysis

We review a neural implementation of the statistical technique of Canonical Correlation Analysis (CCA) and extend it to nonlinear CCA. We then derive the method of kernel-based CCA and compare these two methods on real and artificial data sets before using both on the Blind Separation of Sources.

Colin Fyfe | Pei Ling Lai | P. L. Lai | C. Fyfe

[1] Erkki Oja,et al. The nonlinear PCA learning rule in independent component analysis , 1997, Neurocomputing.

[2] Christian Jutten,et al. Blind separation of sources, part I: An adaptive algorithm based on neuromimetic architecture , 1991, Signal Process..

[3] E. Oja. Simplified neuron model as a principal component analyzer , 1982, Journal of mathematical biology.

[4] Bernhard Schölkopf,et al. Nonlinear Component Analysis as a Kernel Eigenvalue Problem , 1998, Neural Computation.

[5] P.L.Lai,et al. A neural network implementation of canonical correlation analysis , 1999 .

[6] Juha Karhunen,et al. Generalizations of principal component analysis, optimization problems, and neural networks , 1995, Neural Networks.

[7] Colin Fyfe,et al. Canonical correlation analysis using artificial neural networks , 1998, ESANN.

[8] Gunnar Rätsch,et al. Input space versus feature space in kernel-based methods , 1999, IEEE Trans. Neural Networks.

[9] Colin Fyfe,et al. A neural implementation of canonical correlation analysis , 1999, Neural Networks.