Locality Preserving Projection Method Based on Optimal Nearest Neighbor
Locality Preserving Projection(LPP)is a classical dimensionality reduction method used in machine learning.However,the LPP method and some improved methods simply use the k-Nearest Neighbor(k-NN)classification algorithm to find the nearest neighbors of the samples when constructing the local structure of the data,which is easily affected by the parameter k,noise,and outliers.To solve the above problems,a LPP projection method based on the optimal nearest neighbor algorithm is proposed.The proposed method employs the optimal nearest neighbor algorithm to find the sample nearest neighbor points.Then,the algorithm further selects the nearest neighbor samples with a certain number of common points as the optimal nearest neighbors.Then,the algorithm selects the nearest neighbors that are most similar to the samples by limiting the common nearest neighbor points,thereby enhancing the correlation between the nearest neighbor samples.This selection circumvents the problem of the traditional LPP method being greatly influenced by the parameter k.After selecting sufficient sample optimal nearest neighbors,the local structure of the data is constructed to accurately reflect the essential structural features of the data such that dimensionality reduction can retain the effective information of the samples to the maximum extent and improve the performance of the subsequent machine learning models.Comparative experimental results obtained using a public image dataset show that the proposed method has a good data dimensionality reduction effect and effectively improves image recognition accuracy.
Local Preserving Projection(LPP)methodoptimal nearest neighbornearest neighbor sampledimensionality reductionfeature extraction
万方数据
维普
