一种适用于处理中药指纹图谱数据的主成分正交分解算法

An Orthogonal Expansion Algorithm of Principal Component Suitable to Deal with the Fingerprinting Data of Chinese Medicine

中药指纹图谱数据具有变量数很大而样本数较小的特点,本文中采用拉格朗日求极值的方法得到一种新的适合用于处理这类数据的主成分正交分解算法.结果表明:所得到新的算法,在处理中药指纹图谱数据时,与传统的主成分分析算法比较,节省存储单元,计算量小,计算速度快,因而计算效率高.

The fingerprinting data sets of Chinese medicine are the data sets with large number of variables and few objects. A new kind of orthogonal expansion algorithm of Principal Component that is suitable to deal with this kind of data sets has been obtained by use of the Lagrange method of solving extremum problem in this paper. The results indicate that by comparing with the traditional Principal Component Analysis algorithm the new presented algorithm is memory-saving, with small amount of calculation, fast and effective when dealing with the fingerprinting data of Chinese medicine or the data with large number of variables and few objects.