Adaptive Neighboring Selection Algorithm Based on Curvature Prediction in Manifold Learning  

作　　者：Lin Ma Cai-Fa Zhou Xi Liu Yu-Bin Xu 

机构地区：[1]Communication Research Center,Harbin Institute of Technology [2]Science and Technology on Information Transmission and Dissemination in Communication Networks Laboratory

出　　处：《Journal of Harbin Institute of Technology(New Series)》2013年第3期119-123,共5页哈尔滨工业大学学报（英文版）

基　　金：Sponsored by the National Natural Science Foundation of China (Grant No. 61101122 and 61071105);Fundamental Research Funds for the Central Universities (Grant No. HIT. NSRIF. 2010090);Science and Technology on Information Transmission and Dissemination in Communication Networks Laboratory (Grant No. ITD-U12004);Postdoctoral Science Research Development Foundation of Heilongjiang Province (Grant No. LBH-Q12080)

摘　　要：Recently manifold learning algorithm for dimensionality reduction attracts more and more interests, and various linear and nonlinear,global and local algorithms are proposed. The key step of manifold learning algorithm is the neighboring region selection. However,so far for the references we know,few of which propose a generally accepted algorithm to well select the neighboring region. So in this paper,we propose an adaptive neighboring selection algorithm,which successfully applies the LLE and ISOMAP algorithms in the test. It is an algorithm that can find the optimal K nearest neighbors of the data points on the manifold. And the theoretical basis of the algorithm is the approximated curvature of the data point on the manifold. Based on Riemann Geometry,Jacob matrix is a proper mathematical concept to predict the approximated curvature. By verifying the proposed algorithm on embedding Swiss roll from R3 to R2 based on LLE and ISOMAP algorithm,the simulation results show that the proposed adaptive neighboring selection algorithm is feasible and able to find the optimal value of K,making the residual variance relatively small and better visualization of the results. By quantitative analysis,the embedding quality measured by residual variance is increased 45. 45% after using the proposed algorithm in LLE.Recently manifold learning algorithm for dimensionality reduction attracts more and more interests, and various linear and nonlinear,global and local algorithms are proposed. The key step of manifold learning algorithm is the neighboring region selection. However,so far for the references we know,few of which propose a generally accepted algorithm to well select the neighboring region. So in this paper,we propose an adaptive neighboring selection algorithm,which successfully applies the LLE and ISOMAP algorithms in the test. It is an algorithm that can find the optimal K nearest neighbors of the data points on the manifold. And the theoretical basis of the algorithm is the approximated curvature of the data point on the manifold. Based on Riemann Geometry,Jacob matrix is a proper mathematical concept to predict the approximated curvature. By verifying the proposed algorithm on embedding Swiss roll from R3 to R2 based on LLE and ISOMAP algorithm,the simulation results show that the proposed adaptive neighboring selection algorithm is feasible and able to find the optimal value of K,making the residual variance relatively small and better visualization of the results. By quantitative analysis,the embedding quality measured by residual variance is increased 45. 45% after using the proposed algorithm in LLE.

关 键 词：manifold learning curvature prediction adaptive neighboring selection residual variance 

分 类 号：S7[农业科学—林学]