离散过程表征空间维数压缩的熵逼近

An Entropic Approach to Dimensionality Reduction in the Representation Space on Discrete Processes

提出了一种从样本信息开始建立动态随机模型的分类判别方法。这种方法是通过对特征值的研究 ,追求表征空间的维数压缩。还描述了一种算法 ,用以维数压缩对原始信息重新构造和逼近。用一个理论模型显示了这种方法的很大的压缩效果和维数压缩逼近的优良性。并简略讨论了两个类的情况 ,提出了重新构造和分类的一种算法。气象应用说明 ,就维数压缩而论 ,结果与一类情况类似 。

This paper generalizes a method for class discrimination with the purpose of formulating dynamic random models starting from sample information.Such technique pursues,through the study of the eigenvalues,the reduction of the dimensionality in the representation space.We also describe an algorithm that allows the reconstruction and the approximation by dimensionality reduction for the original information.An illustration with a theoretical model reveals the great compression power produced by this scheme,as well as the goodness of the approximations by dimensionality reduction.Two class case is briefly discussed and an algorithm for reconstruction and classification is suggested.An application to meteorological data shows results similar to the one class case as far as dimensionality reduction goes.A reasonable classification rate is also obtained.