High-dimensional Data Classification Based on Principal Component Analysis Dimension Reduction and Improved BP Algorithm

Tai-shan YAN, Yi-ting WEN, Wen-bin LI


In order to realize high-dimensional data classification accurately and reduce computation cost and dimension disaster, principal component analysis (PCA) is applied to reduce dimension of high-dimensional data firstly, and then BP neural network is applied to classify. Aiming at the problem of low classification efficiency of traditional BP algorithm, an improved BP algorithm with two times adaptive adjust of training parameters(TA-BP algorithm) is proposed. By two dynamic adjustment of learning parameters, the algorithm has higher learning efficiency. In MATLAB simulation experiment, the improved BP algorithm is applied to classify high-dimensional data after reducing dimension. The experimental results show that the training speed and classification accuracy of high-dimensional data is improved greatly by this method.


High-dimensional data classification, Principal component analysis, Neural network, Improved BP algorithm


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