基于降维BP神经网络的高维数据分类研究

High-dimensional data classification based on dimension reduction of BP neural network

为确保高维数据的神经网络分类精度,提出了先降维后分类的方法。采用主成分分析(PCA)法实现高维数据的降维。通过分析传统BP算法,提出分两步来更新网络权值的扰动BP学习方法。采用MATLAB对降维分类算法的分类精度和误差收敛速度进行分析。仿真结果显示:先降维再采用扰动BP网络进行高维数据分类可大大提高数据的分类精度和训练速度。

To ensure the classification accuracy of the neural network of highdimensional data, it proposes to firstly reduce its dimension and then to do classification. And it in fact achieves the dimension reduction of highdimensional data by Principal Component Analysis （PCA）. By analysis of the traditional BP algorithm, the proposed disturbance BP learning method is divided into two steps to update the network weights. It analyzes the classification accuracy and error convergence rate of the algorithm through MATLAB. The simulation results show that firstly reducing its dimension and then doing classification of high dimen sional data employing disturbance BP network can greatly improve the classification accuracy and training speed of data.