A fast fixed-point algorithm for independent component analysis

被引:2734
作者
Hyvarinen, A
Oja, E
机构
[1] Helsinki University of Technology, Lab. of Comp. and Info. Science, Espoo
关键词
D O I
10.1162/neco.1997.9.7.1483
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
We introduce a novel fast algorithm for independent component analysis, which can be used for blind source separation and feature extraction. We show how a neural network learning rule can be transformed into a fixed-point iteration, which provides an algorithm that is very simple, does not depend on any user-defined parameters, and is fast to converge to the most accurate solution allowed by the data. The algorithm finds, one at a time, all nongaussian independent components, regardless of their probability distributions. The computations can be performed in either batch mode or a semiadaptive manner. The convergence of the algorithm is rigorously proved, and the convergence speed is shown to be cubic. Some comparisons to gradient-based algorithms are made, showing that the new algorithm is usually 10 to 100 times faster, sometimes giving the solution in just a few iterations.
引用
收藏
页码:1483 / 1492
页数:10
相关论文
共 16 条
[1]  
AMARI S, 1996, ADV NEURAL INFORMATI, V8
[2]  
BELL A, IN PRESS VISION RES
[3]   AN INFORMATION MAXIMIZATION APPROACH TO BLIND SEPARATION AND BLIND DECONVOLUTION [J].
BELL, AJ ;
SEJNOWSKI, TJ .
NEURAL COMPUTATION, 1995, 7 (06) :1129-1159
[4]   Learning the higher-order structure of a natural sound [J].
Bell, AJ ;
Sejnowski, TJ .
NETWORK-COMPUTATION IN NEURAL SYSTEMS, 1996, 7 (02) :261-267
[5]  
Cardoso J.-F., 1990, P IEEE INT C ACOUSTI, P2655
[6]  
CARDOSO JF, 1992, P EUSIPCO, P739
[7]   INDEPENDENT COMPONENT ANALYSIS, A NEW CONCEPT [J].
COMON, P .
SIGNAL PROCESSING, 1994, 36 (03) :287-314
[8]   ADAPTIVE BLIND SEPARATION OF INDEPENDENT SOURCES - A DEFLATION APPROACH [J].
DELFOSSE, N ;
LOUBATON, P .
SIGNAL PROCESSING, 1995, 45 (01) :59-83
[9]  
HURRI J, 1996, P NORSIG 96
[10]  
Hyvarinen A., 1996, P IEEE INT C NEUR NE, P62