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
An Algorithm for Finding Best Matches in Logarithmic Expected Time
ACM Transactions on Mathematical Software (TOMS)
A Fast Nearest-Neighbor Algorithm Based on a Principal Axis Search Tree
IEEE Transactions on Pattern Analysis and Machine Intelligence
An Efficient k-Means Clustering Algorithm: Analysis and Implementation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Optimal Cluster Preserving Embedding of Nonmetric Proximity Data
IEEE Transactions on Pattern Analysis and Machine Intelligence
Fast k-nearest-neighbor search based on projection and triangular inequality
Pattern Recognition
New Algorithms for Efficient High-Dimensional Nonparametric Classification
The Journal of Machine Learning Research
A Branch and Bound Algorithm for Computing k-Nearest Neighbors
IEEE Transactions on Computers
Improvement of the fast exact pairwise-nearest-neighbor algorithm
Pattern Recognition
Boosting k-nearest neighbor classifier by means of input space projection
Expert Systems with Applications: An International Journal
Nearest neighbor pattern classification
IEEE Transactions on Information Theory
K Nearest Neighbor Equality: Giving equal chance to all existing classes
Information Sciences: an International Journal
A generalized cluster centroid based classifier for text categorization
Information Processing and Management: an International Journal
Fuzzy nearest neighbor algorithms: Taxonomy, experimental analysis and prospects
Information Sciences: an International Journal
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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. In this paper, a novel fast kNN search method using an orthogonal search tree is proposed. The proposed method creates an orthogonal search tree for a data set using an orthonormal basis evaluated from the data set. To find the kNN for a query point from the data set, projection values of the query point onto orthogonal vectors in the orthonormal basis and a node elimination inequality are applied for pruning unlikely nodes. For a node, which cannot be deleted, a point elimination inequality is further used to reject impossible data points. Experimental results show that the proposed method has good performance on finding kNN for query points and always requires less computation time than available kNN search algorithms, especially for a data set with a big number of data points or a large standard deviation.