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Research of neural network algorithm based on factor analysis and cluster analysis

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Abstract

Aiming at the large sample with high feature dimension, this paper proposes a back-propagation (BP) neural network algorithm based on factor analysis (FA) and cluster analysis (CA), which is combined with the principles of FA and CA, and the architecture of BP neural network. The new algorithm reduces the feature dimensionality of the initial data through FA to simplify the network architecture; then divides the samples into different sub-categories through CA, trains the network so as to improve the adaptability of the network. In application, it is first to classify the new samples, then using the corresponding network to predict. By an experiment, the new algorithm is significantly improved at the aspect of its prediction precision. In order to test and verify the validity of the new algorithm, we compare it with BP algorithms based on FA and CA.

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Acknowledgments

This work is supported by the Basic Research Program (Natural Science Foundation) of Jiangsu Province of China under grant no. BK2009093, and the National Nature Science Foundation of China under grant no. 60975039.

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Authors and Affiliations

  1. School of Computer Science and Technology, China University of Mining and Technology, Xuzhou, 221008, China

    Shifei Ding, Weikuan Jia, Chunyang Su, Liwen Zhang & Lili Liu

  2. Key Laboratory of Intelligent Information Processing, Institute of Computing Technology, Chinese Academy of Sciences, Beijing, 100080, China

    Shifei Ding

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  1. Shifei Ding
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  2. Weikuan Jia
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  3. Chunyang Su
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  4. Liwen Zhang
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  5. Lili Liu
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Correspondence to Shifei Ding.

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Ding, S., Jia, W., Su, C. et al. Research of neural network algorithm based on factor analysis and cluster analysis. Neural Comput & Applic 20, 297–302 (2011). https://doi.org/10.1007/s00521-010-0416-2

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  • DOI: https://doi.org/10.1007/s00521-010-0416-2

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