Local Fisher Discriminant Component Hashing for Fast Nearest Neighbor Classification

Abstract

This paper presents a novel approximate nearest neighbor classification scheme, Local Fisher Discriminant Component Hashing (LFDCH). Nearest neighbor (NN) classification is a popular technique in the field of pattern recognition but has poor classification speed particularly in high-dimensional space. To achieve fast NN classification, Principal Component Hashing (PCH) has been proposed, which searches the NN patterns in low-dimensional eigenspace using a hash algorithm. It is, however, difficult to achieve accuracy and computational efficiency simultaneously because the eigenspace is not necessarily the optimal subspace for classification. Our scheme, LFDCH, introduces Local Fisher Discriminant Analysis (LFDA) for constructing a discriminative subspace for achieving both accuracy and computational efficiency in NN classification. Through experiments, we confirmed that LFDCH achieved faster and more accurate classification than classification methods using PCH or ordinary NN.Keywordsapproximate nearest neighbor classificationhigh- dimensional spacehashdimensionality reduction