Generalized Regression Neural Network Optimized by Particle Swarm Optimization for the Out-of-Sample Extension Problem in Manifold Learning
Manifold learning has been successfully applied in th e field of dimensionality reduction and data visualization.However,when it is used for supervised classification,results are unsatisfactory.The out-of-sample extension problem is a critical issue that must be properly solved when manifold learning is used for supervised classification.To cope with the problem men-tioned above,a particle swarm-optimized generalized regression neural network is proposed to calculate the low-dimensional em-bedding of the test samples.The low-dimensional embedding of the test samples can be directly used for supervised classification.The proposed algorithm can obtain higher prediction performance through the excellent global search capability of particle swarm optimization,thus it can obtain better prediction performance regarding the out-of-sample extension problem.The author con-ducted experiments on three publicly available benchmark datasets,namely the Diabetes,Iris,and Sonar datasets.The overall accu-racy obtained by the proposed algorithm is 77.63%,100%,and 88.89%,respectively.The proposed algorithm significantly outper-formed eight classification methods in terms of the overall accuracy.Experimental results demonstrate the feasibility and effective-ness of the proposed algorithm.The algorithm can significantly reduce data complexity and improve accuracy for prediction,pat-tern classification,and machine learning.
particle swarm optimizationgeneralized regression neural networkmanifold learningdimensionality reductionout-of-sample extension problem
万方数据
维普
