Large Margin Discriminant Hashing for Fast k-Nearest Neighbor Classification

Abstract

Since the k-nearest neighbor (k-NN) classification is computationally demanding in terms of time and memory, approximate nearest neighbor (ANN) algorithms that utilize dimensionality reduction and hashing are gathering interest. Dimensionality reduction saves memory usage for storing training patterns and hashing techniques significantly reduce the computation required for distance calculation. Several ANN methods have been proposed which make k-NN classification applicable to those tasks that have a large number of training patterns with very high-dimensional feature. Though conventional ANN methods try to approximate Euclidean distance calculation in the original high-dimensional feature space with much lower-dimensional subspace, the Euclidean distance in the original feature space is not necessarily optimal for classification. According to the recent studies, metric learning is effective to improve accuracy of the k-NN classification. In this paper, Large Margin Discriminative Hashing (LMDH) method, which projects input patterns into low dimensional subspace with the optimized metric for the k-NN classification, is proposed.