K-local hyperplane distance nearest neighbor classifier oriented local discriminant analysis

Abstract

K-local hyperplane distance nearest neighbor (HKNN) classifier is an improved K-nearest neighbor (KNN) algorithm that has been successfully applied to pattern classification. This paper embeds the decision rule of HKNN classifier into the discriminant analysis model to develop a new feature extractor. The obtained feature extractor is called K-local hyperplane distance nearest neighbor classifier oriented local discriminant analysis (HOLDA), in which a regularization item is imposed on the original HKNN algorithm to obtain a more reliable distance metric. Based on this distance metric, the homo-class and hetero-class local scatters are characterized in HOLDA. By maximizing the ratio of the hetero-class local scatter to the homo-class local scatter, we obtain a subspace which is suitable for feature extraction and classification. In general, this paper provides a framework for building a feature extractor from the decision rule of a classifier. By this means, the feature extractor and classifier can be seamlessly integrated. Experimental results on four databases demonstrate that the integrated pattern recognition system is effective.