Newton-like methods for nonparametric independent component analysis

被引:0
|
作者
Shen, Hao [1 ]
Hueper, Knut
Smola, Alexander J.
机构
[1] Natl ICT Australia, Syst Engn & Complex Syst Res Program, Canberra, ACT 2612, Australia
[2] Natl ICT Australia, Stat Machine Learning Res Program, Canberra, ACT 2612, Australia
[3] Australian Natl Univ, Res Sch Informat Sci & Engn, Dept Informat Engn, Canberra, ACT 0200, Australia
[4] Australian Natl Univ, Res Sch Informat Sci & Engn, Comp Sci Lab, Canberra, ACT 0200, Australia
来源
NEURAL INFORMATION PROCESSING, PT 1, PROCEEDINGS | 2006年 / 4232卷
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D O I
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中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
The performance of ICA algorithms significantly depends on the choice of the contrast function and the optimisation algorithm used in obtaining the demixing matrix. In this paper we focus on the standard linear nonparametric ICA problem from an optimisation point of view. It is well known that after a pre-whitening process, the problem can be solved via an optimisation approach on a suitable manifold. We propose an approximate Newton's method on the unit sphere to solve the one-unit linear nonparametric ICA problem. The local convergence properties are discussed. The performance of the proposed algorithms is investigated by numerical experiments.
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页码:1068 / 1077
页数:10
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