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
A local mean-based nonparametric classifier
Pattern Recognition Letters
Fast k-nearest-neighbor search based on projection and triangular inequality
Pattern Recognition
Generative models for similarity-based classification
Pattern Recognition
Fast exact k nearest neighbors search using an orthogonal search tree
Pattern Recognition
Fast k-nearest neighbors search using modified principal axis search tree
Digital Signal Processing
Nearest neighbour group-based classification
Pattern Recognition
IEEE Transactions on Knowledge and Data Engineering
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
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The local-mean based classification (LMC) algorithm is an effective classification method. It can reduce the influence of outliers and can achieve better result than most classification algorithms. To classify a test sample with unknown class using the LMC algorithm is very time consuming. To overcome this problem, this paper presents a fast exact LMC method to reduce the computation time of the LMC algorithm using a class rejection process. When the proposed method cooperates with fast nearest k neighbors finding algorithms, an initial distance assigning process is also applied to improve the performance of rejecting impossible samples. Experimental results show that the proposed method can effectively reduce the computation time of the LMC method and can improve the capability of rejecting impossible samples when a fast kNN search method is applied to the LMC algorithm.