基于SCEC的信息特征压缩算法

Information Feature Compression Algorithm Based on SCEC

对交互熵理论进行了研究,提出了对称交互熵的概念,并论证了它是一种距离测度,可以用以度量两个随机变量的差异程度,我们把它作为信息特征压缩的类别可分性判据,称之为对称交互熵判据(SCEC),建立了基于SCEC的信息特征压缩算法.模拟应用表明,提出的算法是一种有效的、可靠的算法,为模式识别理论的研究提供了一种新的数据压缩方法.

The cross entropy theory is discussed, a new concept of Symmetric Cross Entropy Criterion (SCEC) is proposed and proved that SCEC is a distance measure. SCE can be used to measure the difference degree between the random variables and regarded it as class separability criterion for information feature compression, and called Symmetric Cross Entropy Criterion (SCEC). Based on SCEC, a new algorithm of information feature compression is set up. The rersults of simulation application show that the algorithm proposed here is effective and reliable. It provides a new reasearch approach of data compression for pattern recognition.