Abstract A modified K-nearest neighbor based decision boundary analysis (KNN-DBA) method is proposed for improving the classification performance. The decision boundary is determined by K-nearest neighbor classifier which is simple and fast. The extracted feature dimensionality is not limited by class number. Experimental results on the USPS handwritten digit dataset using nearest neighbor and support vector classifiers show that the DBA method outperforms principal component analysis (PCA).
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2007, Vol. 20 
