A Novel Supervised Information Feature Compression Algorithm
Part of the
Lecture Notes in Computer Science
book series (LNCS, volume 3991)
In this paper, a novel supervised information feature compression algorithm is set up. Firstly, according to the information theories, we carried out analysis for the concept and its properties of the cross entropy, then put forward a kind of lately concept of symmetry cross entropy (SCE), and point out that the SCE is a kind of distance measure, which can be used to measure the difference of two random variables. Secondly, We make the SCE separability criterion of the classes for information feature compression, and design a novel algorithm for information feature compression. At last, the experimental results demonstrate that the algorithm here is valid and reliable, and provides a new research approach for feature compression, data mining and pattern recognition.
KeywordsFeature Selection Feature Extraction Probability Vector Feature Selection Algorithm Separability Criterion
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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