Multiscale random projections for compressive classification
Author
Duarte, Marco F.
Davenport, Mark A.
Wakin, Michael B.
Laska, Jason N.
Takhar, Dharmpal
Kelly, Kevin F.
Baraniuk, Richard G.
Type
Journal article
Citation
Abstract
We propose a framework for exploiting dimension-reducing random projections in detection and classification problems. Our approach is based on the generalized likelihood ratio test;
in the case of image classification, it exploits the fact that a set of images of a fixed scene under varying articulation parameters forms a low-dimensional, nonlinear manifold. Exploiting recent results showing that random projections stably embed a smooth manifold in a lower-dimensional space, we develop the multiscale smashed filter as a compressive analog of the familiar matched filter classifier. In a practical target classification problem using a single-pixel camera that directly acquires
compressive image projections, we achieve high classification rates using many fewer measurements than the dimensionality of the images.
Date
2007-09-01Metadata
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- DSP Publications [508]