Faster independent component analysis by preconditioning with hessian approximations

被引：96

作者：

Ablin, Pierre ^{[1
]}

Cardoso, Jean-Francois ^{[2
]}

Gramfort, Alexandre ^{[1
]}

机构：

[1] Inria, Parietal Team, Université Paris-Saclay, Saclay,91400, France

[2] Institut d'Astrophysique de Paris, Centre National de la Recherche Scientifique, Paris,75016, France

来源：

IEEE Transactions on Signal Processing | 2018年 / 66卷 / 15期

基金：

欧洲研究理事会; 欧盟地平线“2020”;

关键词：

Independent component analysis - Approximation algorithms - Maximum likelihood estimation - Newton-Raphson method;

D O I：

10.1109/TSP.2018.2844203

中图分类号：

学科分类号：

摘要：

Independent Component Analysis (ICA) is a technique for unsupervised exploration of multichannel data that is widely used in observational sciences. In its classic form, ICA relies on modeling the data as linear mixtures of nonGaussian independent sources. The maximization of the corresponding likelihood is a challenging problem if it has to be completed quickly and accurately on large sets of real data. This problem has been addressed by resorting to quasi-Newton methods, which rely on sparse approximations of the Hessian of the log-likelihood. However, those approximations are not accurate when the ICA model does not hold exactly, as is often the case for real datasets. We propose a new algorithm, dubbed Picard, which makes use of sparse approximate Hessians only as a preconditioner to the L-BFGS algorithm, refining the Hessian approximation from a memory of the past iterates. Extensive numerical comparisons to several algorithms of the same class demonstrate the superior performance of the proposed technique, especially on real data. © 1991-2012 IEEE.

引用

页码：4040 / 4049