Independent Components Analysis with the JADE algorithm

被引：157
作者：
Rutledge, D. N. [1 ]
Bouveresse, D. Jouan-Rimbaud
机构：
[1] AgroParisTech, UMR Ingn Proc Aliments 1145, F-75005 Paris, France
来源：
TRAC-TRENDS IN ANALYTICAL CHEMISTRY | 2013年 / 50卷
关键词：
Chemometrics; Complex data set; Independence; Independent Components Analysis (ICA); Interpretable signal; Joint Approximate Diagonalization of Eigenmatrices (JADE); Multiway data array; Parallel Factor Analysis (PARAFAC); Principal Components Analysis (PCA); Three-way data; ANALYTICAL-CHEMISTRY; RESOLUTION;
D O I：
10.1016/j.trac.2013.03.013
中图分类号：
O65 [分析化学];
学科分类号：
070302 ; 081704 ;
摘要：
Independent Components Analysis (ICA) is a relatively recent method, with an increasing number of applications in chemometrics. Of the many algorithms available to compute ICA parameters, the Joint Approximate Diagonalization of Eigenmatrices (JADE) algorithm is presented here in detail. Three examples are used to illustrate its performance, and highlight the differences between ICA results and those of other methods, such as Principal Components Analysis. A comparison with Parallel Factor Analysis (PARAFAC) is also presented in the case of a three-way data set to show that ICA applied on an unfolded high-order array can give results comparable with those of PARAFAC. (c) 2013 Elsevier Ltd. All rights reserved.
引用
收藏
页码：22 / 32
页数：11
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