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20 September 2007 Automated discrimination of shapes in high dimensions
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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.
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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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