关键词: K近邻分类, 相关性, 去除噪声样本, 局部保持投影, 稀疏学习

Abstract:

Traditional K Nearest Neighbors (KNN) classification method has drawbacks such as no elimination of noise samples,no manifold structure preservation of the samples, and no consideration of the correlation between samples.To solve these problems,we propose an efficient SAKNN algorithm with adaptive K value.Sparse learning theory is introduced and we reconstruct each test sample with the training samples for KNN classification. We introduce an  l2,1 norm to remove the noisy samples,employ the Locality Preserving Projections (LPP) to keep the data structures,and makes the best use of the correlation between the samples in the reconstruction process.With these technologies we can get the transformation matrix W and in turn determine the value of K.Simulation results on the UCI data sets demonstrate a better classification accuracy than the traditional KNN and the EntropyKNN method.

Key words: K nearest neighbor (KNN) classification;correlation;removal of noise samples;locality preserving projection;sparse learning