Automated discrimination of shapes in high dimensions
Linh H. Lieu, Naoki Saito · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2007
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.