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.