Dept. of Computer Science, University of Helsinki
Learning Natural Image Structure with a Horizontal Product Model
Thursday 05th of March 2009 at 12:00pm
We present a novel extension to Independent Component Analysis (ICA), where the data is generated as the product of two submodels, each of which follow an ICA model, and which combine in a horizontal fashion. This is in contrast to previous nonlinear extensions to ICA which were based on a hierarchy of layers. We apply the product model to natural image patches and report the emergence of localized masks in the additional network layer, while the Gabor features that are obtained in the primary layer change their tuning properties and become less localized. As an interpretation we suggest that the model learns to separate the localization of image features from other properties, since identity and position of a feature are plausibly independent. We also show that the horizontal model can be interpreted as an overcomplete model where the features are no longer independent.
508-20 Evans Hall
Join Email List
You can subscribe to our weekly seminar email list by sending an email to
firstname.lastname@example.org that contains the words
subscribe redwood in the body of the message.
(Note: The subject line can be arbitrary and will be ignored)