Automated discrimination of shapes in high dimensions (with L. Lieu), Wavelets XII (D. Van De Ville, V. K. Goyal, and M. Papadakis, eds.), Proc. SPIE 6701, Paper #67011V, 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.

  • Get the full paper: PDF file.
  • Get the official version via doi:10.1117/12.734657.

  • Please email me if you have any comments or questions!
    Go back to Naoki's Publication Page