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Novel Online Dimensionality Reduction Method with Improved Topology Representing and Radial Basis Function Networks

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  • Shengqiao Ni
  • Jiancheng Lv
  • Zhehao Cheng
  • Mao Li

Abstract

This paper presents improvements to the conventional Topology Representing Network to build more appropriate topology relationships. Based on this improved Topology Representing Network, we propose a novel method for online dimensionality reduction that integrates the improved Topology Representing Network and Radial Basis Function Network. This method can find meaningful low-dimensional feature structures embedded in high-dimensional original data space, process nonlinear embedded manifolds, and map the new data online. Furthermore, this method can deal with large datasets for the benefit of improved Topology Representing Network. Experiments illustrate the effectiveness of the proposed method.

Suggested Citation

  • Shengqiao Ni & Jiancheng Lv & Zhehao Cheng & Mao Li, 2015. "Novel Online Dimensionality Reduction Method with Improved Topology Representing and Radial Basis Function Networks," PLOS ONE, Public Library of Science, vol. 10(7), pages 1-26, July.
    • Handle: RePEc:plo:pone00:0131631
    DOI: 10.1371/journal.pone.0131631
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    References listed on IDEAS

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    1. Hai-Ming Xu & Xi-Wei Sun & Ting Qi & Wan-Yu Lin & Nianjun Liu & Xiang-Yang Lou, 2014. "Multivariate Dimensionality Reduction Approaches to Identify Gene-Gene and Gene-Environment Interactions Underlying Multiple Complex Traits," PLOS ONE, Public Library of Science, vol. 9(9), pages 1-12, September.
    2. Liang Tang & Silong Peng & Yiming Bi & Peng Shan & Xiyuan Hu, 2014. "A New Method Combining LDA and PLS for Dimension Reduction," PLOS ONE, Public Library of Science, vol. 9(5), pages 1-10, May.
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