Research on Stereographic Projection and It’s Application on Feed Forward Neural Network

  • Zhenya Zhang
  • Hongmei Cheng
  • Xufa Wang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4221)


Feed forward neural network for classification instantly requires that the modular length of input vector is 1. On the other hand, Stereographic projection can map a point in n dimensional real space into the surface of unit sphere in (n+1) dimensional real space. Because the modular length of any point in the unit sphere of (n+1) dimensional real surface is 1 and stereographic projection is a bijective mapping, Stereographic projection can be treated as an implementation for the normalization of vector in n dimensional real space. Experimental results shown that feed forward neural network can classify data instantly and accurately if stereographic projection is used to normalized input vector for feed forward network.


Unit Sphere Feed Forward Neural Network Stereographic Projection Feed Forward Network Bijective Mapping 


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  1. 1.
    Ikonomakis, M., Kotsiantis, S., Tampakas, V.: Text Classification Using Machine Learning Techniques. Wseas Transactions on Computers 4(8), 966–974 (2005)Google Scholar
  2. 2.
    Shu, B., Kak, S.: A neural network-based intelligent meta search engine. Information Sciences 120(1), 1–11 (1999)CrossRefGoogle Scholar
  3. 3.
    Zhang, Z., Zhang, S., Wang, X., Chen, E., Cheng, H.: TextCC: New Feed Forward Neural Network for Classifying Documents Instantly. In: Wang, J., Liao, X.-F., Yi, Z. (eds.) ISNN 2005. LNCS, vol. 3497, pp. 232–237. Springer, Heidelberg (2005)Google Scholar
  4. 4.
    Brin, S., Page, L.: Anatomy of a large scale hypertextual web search engine. In: Proc. of the Seventh International World Wide Web Conference, pp. 107–117. Amsterdam (1998)Google Scholar
  5. 5.
    Gudivada, V.N., Raghavan, V.V., Grosky, W.I.: Information retrieval on the world wide web. IEEE Internet Computing 1(5), 59–68 (1997)CrossRefGoogle Scholar
  6. 6.
    Arwar. Item-Based collaborative filtering recommendation algorithms. In: Proceedings of the 10th International World Wide Web Conference (WWW10), pp. 285–295 (2001)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Zhenya Zhang
    • 1
    • 3
  • Hongmei Cheng
    • 2
  • Xufa Wang
    • 4
  1. 1.Institute of Architecture & Industry (AIAI)Computer and Information Engineering Department of AnhuiHefeiChina
  2. 2.Management Engineering Department of AIAIHefeiChina
  3. 3.MOE-Microsoft Key Laboratory of Multimedia Computing and CommunicationUniversity of Science and Technology of China (USTC)HefeiChina
  4. 4.Computer Science Department of USTCHefeiChina

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