南京大学学报（自然科学版）Issue(4)：741-748,8.DOI:10.13232/j.cnki.jnju.2015.04.012
基于流形结构邻域选择的局部投影近邻传播算法
Locality preserving projections affinity propagation plgorithm based on manifold structure neighborhood selection
摘要
Abstract
Affinity propagation (AP )algorithm is a fast and effective clustering method.Compared with other traditional clustering algorithms,the AP algorithm treats each data point as the candidate of the representative point to avoid the clustering results limiting in the choice of initial representative point.At the same time,the algorithm does not need the symmetry of the similarity matrix generated in the dataset with high operation speed in dealing with large-scale multi class data.Hence,AP algorithm can effectively solve the problem of non Euclidean space and large sparse matrix calculation.Due to the great advantage of the AP algorithm in clustering,it is widely applied in pattern recognition,web mining,biomedical and multi target detection,and is becoming a necessary method of data a-nalysis.In order to well determine bias parameter of AP algorithm without prior knowledge,a novel method called silhouette clustering validity index is utilized to determine the parameter in this paper.The problem of information overlap is the main drawback of AP algorithm in dealing with complex structure or high dimensional data for clustering.In order to resolve the above problem,we propose an approaching algorithm which combines the locality preserving projections(LPP)method and the AP algorithm.It deletes the redundant information in the data space under the condition of effectively keeping the data inner nonlinear structure.The experiment results verify its accuracy and effectiveness and shows that the performance of the proposed algorithm is better than the traditional AP algorithm.关键词
近邻传播算法/局部保持投影/Silhouette 指标/邻域选择/流形距离Key words
affinity propagation(AP)/locality preserving projections(LPP)/silhouette index/neighborhood selection/manifold distance分类
信息技术与安全科学引用本文复制引用
周治平,张道文,王杰锋,孙子文..基于流形结构邻域选择的局部投影近邻传播算法[J].南京大学学报（自然科学版）,2015,(4):741-748,8.基金项目
江苏省产学研联合创新资金-前瞻性联合研究项目(BY2013015-33),江苏省自然科学基金(BK20131107) （BY2013015-33）
