Abstract principal component analysis

被引：0
|
作者：
TianJiang Li
Qiang Du
机构：
[1] The Pennsylvania State University,Department of Mathematics
[2] CGG,Lab of Applied Mathematics
[3] Beijing Computational Science Research Center,undefined
来源：
Science China Mathematics | 2013年 / 56卷
关键词：
abstract principal component analysis; pattern recognition; mode extraction; reduced order modeling; traveling waves; 65D99; 62H30; 68T10; 65M99;
D O I：
暂无
中图分类号：
学科分类号：
摘要：
We present the basic idea of abstract principal component analysis (APCA) as a general approach that extends various popular data analysis techniques such as PCA and GPCA. We describe the mathematical theory behind APCA and focus on a particular application to mode extractions from a data set of mixed temporal and spatial signals. For illustration, algorithmic implementation details and numerical examples are presented for the extraction of a number of basic types of wave modes including, in particular, dynamic modes involving spatial shifts.
引用
收藏
页码：2783 / 2798
页数：15
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