Examining the Dependencies between Ica Features of Image Data
نویسنده
چکیده
In this paper we study the dependencies of features found by independent component analysis (ICA) in image data by examining how the activation of one feature changes certain statistics of the data. We look at how the PCA components are affected when we know a certain ICA feature is highly active, and also study the ICA components in this situation. This can be thought of as a simple form of two-layer ICA. We show that the activation level of features with similar properties is elevated, and the activation level of features with distinctly different properties is decreased.
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