An algorithm for finding nearest neighbours in (approximately) constant average time
Pattern Recognition Letters
Approximate nearest neighbors: towards removing the curse of dimensionality
STOC '98 Proceedings of the thirtieth annual ACM symposium on Theory of computing
An optimal algorithm for approximate nearest neighbor searching fixed dimensions
Journal of the ACM (JACM)
Data structures and algorithms for nearest neighbor search in general metric spaces
SODA '93 Proceedings of the fourth annual ACM-SIAM Symposium on Discrete algorithms
Near Neighbor Search in Large Metric Spaces
VLDB '95 Proceedings of the 21th International Conference on Very Large Data Bases
Locality-sensitive hashing scheme based on p-stable distributions
SCG '04 Proceedings of the twentieth annual symposium on Computational geometry
Face Recognition by Projection-based 3D Normalization and Shading Subspace Orthogonalization
FGR '06 Proceedings of the 7th International Conference on Automatic Face and Gesture Recognition
Dimensionality Reduction of Multimodal Labeled Data by Local Fisher Discriminant Analysis
The Journal of Machine Learning Research
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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.