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Kernel entropy component analysis: New theory and semi-supervised learning

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1 Author(s)
Robert Jenssen ; Department of Physics and Technology, University of Tromsø, Norway

A new theory for kernel entropy component analysis (kernel ECA) is developed, based on distribution dependent convolution operators, ensuring the validity of the method for any positive semi-definite kernel. Furthermore, a new semi-supervised kernel ECA classification method is derived with positive results compared to the state-of-the-art.

Published in:

2011 IEEE International Workshop on Machine Learning for Signal Processing

Date of Conference:

18-21 Sept. 2011