ICA 2007

ICA 2007
7th International Conference on
Independent Component Analysis
and Signal Separation

London, UK        9 - 12 September 2007

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Paper No: 70

Learning of translation-invariant independent components: Multivariate anechoic Mixtures

Author(s): Lars Omlor, Martin A. Giese

Abstract

For the extraction of sources with unsupervised learning techniques invariance under certain transformations, such as shifts, rotations or scaling, is often a desirable property. A straight-forward approach for accomplishing this goal is to include these transformations and its parameters into the mixing model. For the case of one-dimensional signals in presence of shifts this problem has been termed anechoic demixing, and several algorithms for the analysis of time series have been proposed. Here, we generalize this approach for sources depending on multidimensional arguments and apply it for learning of translation-invariant features from higher-dimensional data, such as images. A new algorithm for the solution of such high-dimensional anechoic demixing problems based on the Wigner-Ville distribution is presented. It solves the multidimensional problem by projection onto multiple one-dimensional problems. The feasibility of this algorithm is demonstrated by learning independent features from sets of real images

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