A new approach to clustering and object detection with independent component analysis

Authors:
Ingo R. Keck;Salua Nassabay;Carlos G. Puntonet;Elmar W. Lang
Affiliations:
Institute of Biophysics, Neuro- and Bioinformatics Group, University of Regensburg, Regensburg, Germany;Departamento de Arquitectura y Tecnologia de Computadores, Universidad de Granada/ESII, Granada, Spain;Departamento de Arquitectura y Tecnologia de Computadores, Universidad de Granada/ESII, Granada, Spain;Institute of Biophysics, Neuro- and Bioinformatics Group, University of Regensburg, Regensburg, Germany
Venue:
IWINAC'05 Proceedings of the First international work-conference on the Interplay Between Natural and Artificial Computation conference on Artificial Intelligence and Knowledge Engineering Applications: a bioinspired approach - Volume Part II
Year:
2005

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Abstract

It has previously been suggested that the visual cortex performs a data analysis similar to independent component analysis (ICA). Following this idea we show that an incomplete ICA, applied after filtering, can be used to detect objects in natural scenes. Based on this we show that an incomplete ICA can be used to efficiently cluster independent components. We further apply this algorithm to toy data and a real-world fMRI data example and show that this approach to clustering offers a wide variety of applications.