Dimensionality Reduction Using External Context in Pattern Recognition Problems with Ordered Labels

Our aim is to propose a new look at the dimensionality reduction in pattern recognition problems by extracting part of variables that are further called external context variables. We show how to incorporate them into the Bayes classification scheme with loss functions that depend on class labels that are ordered. Then, the general form of the optimal context sensitive classifier is derived and the learning method that is based on kernel approximation is proposed.

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