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
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Ding, S., Ning, W., Jin, F., Xia, S., Shi, Z. (2007). Supervised Information Feature Compression Algorithm Based on Divergence Criterion. In: Huang, DS., Heutte, L., Loog, M. (eds) Advanced Intelligent Computing Theories and Applications. With Aspects of Artificial Intelligence. ICIC 2007. Lecture Notes in Computer Science(), vol 4682. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74205-0_95
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DOI: https://doi.org/10.1007/978-3-540-74205-0_95
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-74201-2
Online ISBN: 978-3-540-74205-0
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