Information cut for clustering using a gradient descent approach

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. Key words: Graph theoretic cut, information theory, Parzen window density estimation, clustering, gradient descent optimization, annealing.

• Type: Journal
• Year: 2007
• Where: PR
• Authors: Robert Jenssen, Deniz Erdogmus, Kenneth E. Hild II, Jose C. Principe, Torbjørn Eltoft
• Keywords: Graph-based Methods, Information Cut, PR 2007, Theoretic Distance Measure