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Proceedings Paper

Optimal frame pursuit for pattern classification
Author(s): Jason C. Isaacs
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Paper Abstract

Frame methods, basis expansion methods, or kernel methods provide a higher-dimensional representation of a given dataset within a feature space for discrimination applications. Frame pursuit addresses the problem of searching for optimal frames to improve classification for pattern recognition applications. In this paper, the results of two stochastic optimization techniques applied to the optimal frame problem are presented. The cost function is a k-nearest-neighbor function. These techniques are tested here over six datasets. Empirical results demonstrate the utility of frame transformations for improving performance results in pattern recognition applications.

Paper Details

Date Published: 23 May 2011
PDF: 7 pages
Proc. SPIE 8017, Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XVI, 80170N (23 May 2011); doi: 10.1117/12.885641
Show Author Affiliations
Jason C. Isaacs, Naval Surface Warfare Ctr. (United States)


Published in SPIE Proceedings Vol. 8017:
Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XVI
Russell S. Harmon; John H. Holloway; J. Thomas Broach, Editor(s)

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