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Hyper-Ellipsoidal Conjugate Gradient Descent

On this page you can find a MATLAB implementation of the Hyper-Ellipsoidal Conjugate Gradient Descent algorithm, that can be used for sparse opitimization of second order kernel methods like kernel-PCA, kernel-SFA (slow feature analysis), or kernel-CCA (canonical correlation analysis). The following two files you may download, use, redistribute, and/or modify under the terms of the GNU General Public License [1].

hecgd.m [2] - hyper-ellipsoidal conjugate gradient descent algorithm
errsokm.m [3] - error function for sparse second order kernel methods

How to use these files is described here [4].

If you use this software in publications, please cite:

Vollgraf, R. and Obermayer, K. (2006). Sparse Optimization for Second Order Kernel Methods [5]. IJCNN 2006 Conference Proceedings. IEEE, 145 – 152.

------ Links: ------

[1] https://www.ni.tu-berlin.de/fileadmin/fg215/softwar
e/HyperEllips/gpl.txt

[2] https://www.ni.tu-berlin.de/fileadmin/fg215/softwar
e/HyperEllips/hecgd.m

[3] https://www.ni.tu-berlin.de/fileadmin/fg215/softwar
e/HyperEllips/errsokm.m

[4] https://www.ni.tu-berlin.de/fileadmin/fg215/softwar
e/HyperEllips/usage.txt

[5] https://www.ni.tu-berlin.de/menue/software_and_data
/hyper_ellipsoidal_conjugate_gradient_descent/parameter
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