A Neural-Network Dimension Reduction Method for Large-Set Pattern Classification

  • Authors:

  • Yijiang Jin;Shaoping Ma

  • Affiliations:

  • -;-

  • Venue:
  • ICMI '00 Proceedings of the Third International Conference on Advances in Multimodal Interfaces
  • Year:
  • 2000

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Abstract

High-dimensional data are often too complex to be classified. K-L transformation is an effective dimension reduction method. However its result is not satisfactory in large-set pattern classification. In this paper a novel nonlinear dimension reduction method is presented and analyzed. The transform is achieved through a multi-layer feed-forward neural network trained with K-L transformation result. Experimental results show that this method is more effective than K-L transformation being applied in large-set pattern classification such as Chinese character recognition.