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Automated discrimination of shapes in high dimensions

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.