Data Preprocessing

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(MNIST Handwritten Digits)
(ICA-based Models (with orthogonalization))
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For ICA-based models with orthogonalization, it is ''very'' important for the data to be as close to white (identity covariance) as possible. This is a side-effect of using orthogonalization to decorrelate the features learned (more details in [[Independent Component Analysis | ICA]]). Hence, in this case, you will want to use an <tt>epsilon</tt> that is as small as possible (e.g., <math>epsilon = 1e-6</math>).
For ICA-based models with orthogonalization, it is ''very'' important for the data to be as close to white (identity covariance) as possible. This is a side-effect of using orthogonalization to decorrelate the features learned (more details in [[Independent Component Analysis | ICA]]). Hence, in this case, you will want to use an <tt>epsilon</tt> that is as small as possible (e.g., <math>epsilon = 1e-6</math>).
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Revision as of 08:13, 29 April 2011

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