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- W2170191761 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." @default.
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- W2170191761 date "2007-01-01" @default.
- W2170191761 modified "2023-09-26" @default.
- W2170191761 title "Multiscale Random Projections for Compressive Classification" @default.
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- W2170191761 doi "https://doi.org/10.1109/icip.2007.4379546" @default.
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