A Fast k Nearest Neighbor Finding Algorithm Based on the Ordered Partition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Vector quantization and signal compression
Vector quantization and signal compression
A near pattern-matching scheme based upon principal component analysis
Pattern Recognition Letters
Visual learning and recognition of 3-D objects from appearance
International Journal of Computer Vision
A fast branch & bound nearest neighbour classifier in metric spaces
Pattern Recognition Letters
A Simple Algorithm for Nearest Neighbor Search in High Dimensions
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Fast Nearest-Neighbor Algorithm Based on a Principal Axis Search Tree
IEEE Transactions on Pattern Analysis and Machine Intelligence
Optimal Cluster Preserving Embedding of Nonmetric Proximity Data
IEEE Transactions on Pattern Analysis and Machine Intelligence
Hadamard transform based fast codeword search algorithm for high-dimensional VQ encoding
Information Sciences: an International Journal
Fast k-nearest-neighbor search based on projection and triangular inequality
Pattern Recognition
Improvement of the fast exact pairwise-nearest-neighbor algorithm
Pattern Recognition
An efficient encoding algorithm for vector quantization based on subvector technique
IEEE Transactions on Image Processing
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The problem of k-nearest neighbors (kNN) is to find the nearest k neighbors for a query point from a given data set. Among available methods, the principal axis search tree (PAT) algorithm always has good performance on finding nearest k neighbors using the PAT structure and a node elimination criterion. In this paper, a novel kNN search algorithm is proposed. The proposed algorithm stores projection values for all data points in leaf nodes. If a leaf node in the PAT cannot be rejected by the node elimination criterion, data points in the leaf node are further checked using their pre-stored projection values to reject more impossible data points. Experimental results show that the proposed method can effectively reduce the number of distance calculations and computation time for the PAT algorithm, especially for the data set with a large dimension or for a search tree with large number of data points in a leaf node.