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Using Bilevel Feature Extractors to Reduce Dimensionality in Images
May/June 2012 (vol. 14 no. 3)
pp. 60-67
Marc Joliveau, Université de Montréal, Canada
Michel Gendreau, École Polytechnique de Montréal, Canada

A bilevel procedure for dimensionality reduction makes it possible to discover the underlying global geometry of a complex natural observations dataset—such as human handwriting or faces under different viewing positions—with higher precision than existing methods.

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Index Terms:
Visual perception, feature extractor, image processing, pattern recognition, dimensionality reduction, scientific computing
Citation:
Marc Joliveau, Michel Gendreau, "Using Bilevel Feature Extractors to Reduce Dimensionality in Images," Computing in Science and Engineering, vol. 14, no. 3, pp. 60-67, May-June 2012, doi:10.1109/MCSE.2011.55
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