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Feature extraction using problem localization
1Sperry Research Center, Sudbury, MA 01776.
IEEE Transactions on Pattern Analysis and Machine Intelligence
|August 27, 2011
Abstract:
Feature extraction is considered as a mean-quare estimation of the Bayes risk vector. The problem is simplified by partitioning the distribution space into local subregions and performing a linear estimation in each subregion. A modified clustering algorithm is used to fimd the partitioning which minimizes the mean-square error.
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