Information cut for clustering using a gradient descent approach

Authors:
Robert Jenssen;Deniz Erdogmus;Kenneth E. Hild, II;Jose C. Principe;Torbjørn Eltoft
Affiliations:
Department of Physics and Technology, University of Tromsø, N-9037 Tromsø, Norway;Oregon Graduate Institute, OHSU, Portland OR 97006, USA;Biomagnetic Imaging Lab, University of California, SF, CA 94143, USA;Computational NeuroEngineering Laboratory, Department of Electrical and Computer Engineering, University of Florida, FL 32611, USA;Department of Physics and Technology, University of Tromsø, N-9037 Tromsø, Norway
Venue:
Pattern Recognition
Year:
2007

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Abstract

We introduce a new graph cut for clustering which we call the Information Cut. It is derived using Parzen windowing to estimate an information theoretic distance measure between probability density functions. We propose to optimize the Information Cut using a gradient descent-based approach. Our algorithm has several advantages compared to many other graph-based methods in terms of determining an appropriate affinity measure, computational complexity, memory requirements and coping with different data scales. We show that our method may produce clustering and image segmentation results comparable or better than the state-of-the art graph-based methods.