Name
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 |
Year | DOI | Venue |
|---|---|---|
2007 | 10.1007/978-3-540-74205-0_95 | ICIC '07 Proceedings of the 3rd International Conference on Intelligent Computing: Advanced Intelligent Computing Theories and Applications. With Aspects of Artificial Intelligence |
Keywords | Field | DocType |
information feature compression,compression algorithm,information feature compression algorithm,average separability information asi,information theory,distance axiomatization,divergence criterion,discrete divergence,novel supervised information feature,average separability information,symmetry average separability information,average sasi criterion,supervised information feature compression,compression theorem,satisfiability | Information theory,Design information,Divergence,Pattern recognition,Computer science,Artificial intelligence,Data compression,Machine learning,Compression theorem | Conference |
Volume | Issue | ISSN |
4682 LNAI | null | 16113349 |
Citations | PageRank | References |
0 | 0.34 | 3 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Shiei Ding | 1 | 0 | 0.34 |
| Wei Ning | 2 | 0 | 0.68 |
| Fengxiang Jin | 3 | 124 | 10.72 |
| Shixiong Xia | 4 | 102 | 13.28 |
| Zhongzhi Shi | 5 | 2440 | 238.03 |