2007 IEEE International Conference on Image Processing - San Antonio, Texas, U.S.A. - September 16-19, 2007

Technical Program

Paper Detail

Paper:MP-P6.8
Session:Biomedical Imaging I
Time:Monday, September 17, 14:30 - 17:10
Presentation: Poster
Title: EXTENSION OF MUTUAL SUBSPACE METHOD FOR LOW DIMENSIONAL FEATURE PROJECTION
Authors: Dragana Veljkovic; University of Texas at San Antonio 
 Kay Robbins; University of Texas at San Antonio 
 Doug Rubino; University of California, San Diego 
 Nicholas Hatsopoulos; University of Chicago 
Abstract: Face recognition algorithms based on mutual subspace methods (MSM) map segmented faces to single points on a feature manifold and then apply manifold learning techniques to classify the results. This paper proposes a generic extension to MSM for analysis of features in high-throughput recordings. We apply this method to analyze short duration overlapping waves in synthetic data and multielectrode brain recordings. We compare different feature space topologies and projection techniques, including MDS, ISOMAP and Laplacian eigenmaps. Overall we find that ISOMAP shows the least sensitivity to noise and provides the best association between distance in feature space and Euclidean distance in projection space. For non-noisy data, Laplacian eigenmaps show the least sensitivity to feature space topology.



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