Proceedings Paper
Automated discrimination of shapes in high dimensions| Format | Member Price | Non-Member Price |
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Paper 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.
Paper Details
Date Published: 20 September 2007
PDF: 12 pages
Proc. SPIE 6701, Wavelets XII, 67011V (20 September 2007); doi: 10.1117/12.734657
Published in SPIE Proceedings Vol. 6701:
Wavelets XII
Dimitri Van De Ville; Vivek K. Goyal; Manos Papadakis, Editor(s)
PDF: 12 pages
Proc. SPIE 6701, Wavelets XII, 67011V (20 September 2007); doi: 10.1117/12.734657
Show Author Affiliations
Linh Lieu, Univ. of California, Davis (United States)
Naoki Saito, Univ. of California, Davis (United States)
Published in SPIE Proceedings Vol. 6701:
Wavelets XII
Dimitri Van De Ville; Vivek K. Goyal; Manos Papadakis, Editor(s)
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