A Solution to the Curse of Dimensionality Problem in Pairwise Scoring Techniques

This paper provides a solution to the curse of dimensionality problem in the pairwise scoring techniques that are commonly used in bioinformatics and biometrics applications. It has been recently discovered that stacking the pairwise comparison scores between an unknown patterns and a set of known patterns can result in feature vectors with nice discriminative properties for classification. However, such technique can lead to curse of dimensionality because the vectors size is equal to the training set size. To overcome this problem, this paper shows that the pairwise score matrices possess a symmetric and diagonally dominant property that allows us to select the most relevant features independently by an FDA-like technique. Then, the paper demonstrates the capability of the technique via a protein sequence classification problem. It was found that 10-fold reduction in the number of feature dimensions and recognition time can be achieved with just 4% reduction in recognition accuracy.