23 May 2011 Optimal frame pursuit for pattern classification
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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.
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Jason C. Isaacs, "Optimal frame pursuit for pattern classification", Proc. SPIE 8017, Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XVI, 80170N (23 May 2011); doi: 10.1117/12.885641; https://doi.org/10.1117/12.885641
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