Divergence-Based supervised information feature compression algorithm

  • Authors:

  • Shi-Fei Ding;Zhong-Zhi Shi

  • Affiliations:

  • College of Information Science and Engineering, Shandong Agricultural University, Taian, P.R. China;Key Laboratory of Intelligent Information Processing, Institute of Computing Technology, Chinese Academy of Sciences, Beijing, P.R. China

  • Venue:
  • ISNN'06 Proceedings of the Third international conference on Advances in Neural Networks - Volume Part I
  • Year:
  • 2006

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Abstract

In this paper, a novel supervised information feature compression algorithm based on divergence is set up. Firstly, according to the information theory, the concept and its properties of the divergence, i.e. average separability information (ASI) is studied, and a concept of symmetry average separability information (SASI) is proposed, and proved that the SASI here is a kind of distance measure, i.e. the SASI satisfies three requests of distance axiomatization, which can be used to measure the difference degree of a two-class problem. Secondly, based on the SASI, a compression theorem is given, and can be used to design information feature compression algorithm. Based on these discussions, we design a novel supervised information feature compression algorithm based on the SASI. At last, the experimental results demonstrate that the algorithm here is valid and reliable.