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Paper Detail

Paper:	SS-1.4
Session:	Statistical Inferences on Nonlinear Manifolds with Applications in Signal and Image Processing
Time:	Tuesday, May 16, 11:30 - 11:50
Presentation:	Special Session Lecture
Topic:	Special Sessions: Statistical inferences on nonlinear manifolds with applications in signal and image processing
Title:	Dual Rooted-Diffusions for Clustering and Classification on Manifolds
Authors:	Steve Grikschat, University of Michigan, United States; Jose Costa, California Institute of Technology, United States; Alfred O. Hero, III, University of Michigan, United States; Olivier J. J. Michel, Universite de Nice, France
Abstract:	We introduce a new similarity measure between data points suited for clustering and classification on smooth manifolds. The proposed measure is constructed from a dual rooted graph diffusion over the feature vector space, obtained by growing dual rooted minimum spanning trees (MST) between data points. This diffusion model for pairwise affinities naturally accommodates the case where the feature distribution is supported on a lower dimensional manifold. When this affinity measure is combined with labeled data, a semi-supervised classifier can be defined that handles both labeled and unlabeled data in a seamless manner. We will illustrate our method for both simulated ground truth and real partially labeled data sets.