Automated discrimination of shapes in high dimensions

Linh Lieu; Naoki Saito

doi:10.1117/12.734657

20 September 2007 Automated discrimination of shapes in high dimensions

Linh Lieu, Naoki Saito

Proceedings Volume 6701, Wavelets XII; 67011V (2007) https://doi.org/10.1117/12.734657
Event: Optical Engineering + Applications, 2007, San Diego, California, United States

Abstract

We present a new method for discrimination of data classes or data sets in a high-dimensional space. Our approach combines two important relatively new concepts in high-dimensional data analysis, i.e., Diffusion Maps and Earth Mover's Distance, in a novel manner so that it is more tolerant to noise and honors the characteristic geometry of the data. We also illustrate that this method can be used for a variety of applications in high dimensional data analysis and pattern classification, such as quantifying shape deformations and discrimination of acoustic waveforms.

Citation Download Citation

Linh Lieu and Naoki Saito "Automated discrimination of shapes in high dimensions", Proc. SPIE 6701, Wavelets XII, 67011V (20 September 2007); https://doi.org/10.1117/12.734657

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