论文信息 - Feature Extraction Using Problem Localization

Feature Extraction Using Problem Localization

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.

Keinosuke Fukunaga | Robert D. Short

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