Supervised Information Feature Compression Algorithm Based on Divergence Criterion

  • Authors:

  • Shiei Ding;Wei Ning;Fengxiang Jin;Shixiong Xia;Zhongzhi Shi

  • Affiliations:

  • School of Computer Science and Technology, China University of Mining and Technology, Xuzhou 221008, and Key Laboratory of Intelligent Information Processing, Institute of Computing Technology, Ch ...;School of Computer Science and Technology, Xuzhou Normal University, Xuzhou 221116,;College of Geinformation Science and Engineering, Shandong University of Science and Technology, Qingdao 266510,;School of Computer Science and Technology, China University of Mining and Technology, Xuzhou 221008,;Key Laboratory of Intelligent Information Processing, Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100080,

  • Venue:
  • ICIC '07 Proceedings of the 3rd International Conference on Intelligent Computing: Advanced Intelligent Computing Theories and Applications. With Aspects of Artificial Intelligence
  • Year:
  • 2009

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Abstract

In this paper, a novel supervised information feature compression algorithm based on divergence criterion is set up. Firstly, according to the information theory, the concept and its properties of the discrete 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 construct a novel supervised information feature compression algorithm based on the average SASI criterion for multi-class. At last, the experimental results demonstrate that the algorithm here is valid and reliable