What is the relation between slow feature analysis and independent component analysis?

被引：62

作者：

Blaschke, Tobias ^{[1
]}

Berkes, Pietro ^{[1
]}

Wiskott, Laurenz ^{[1
]}

机构：

[1] Humboldt Univ, Inst Theoret Biol, D-10115 Berlin, Germany

来源：

NEURAL COMPUTATION | 2006年 / 18卷 / 10期

关键词：

D O I：

10.1162/neco.2006.18.10.2495

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

We present an analytical comparison between linear slow feature analysis and second-order independent component analysis, and show that in the case of one time delay, the two approaches are equivalent. We also consider the case of several time delays and discuss two possible extensions of slow feature analysis.

引用

页码：2495 / 2508

页数：14

共 27 条

[21]

Pham DT, 1997, IEEE T SIGNAL PROCES, V45, P1712, DOI 10.1109/78.599941

[22]

STONE JV, 1995, NETWORK, V6, P1

[23] Independent component filters of natural images compared with simple cells in primary visual cortex [J].

van Hateren, JH ;

van der Schaaf, A .

PROCEEDINGS OF THE ROYAL SOCIETY B-BIOLOGICAL SCIENCES, 1998, 265 (1394) :359-366

[24] Slow feature analysis: A theoretical analysis of optimal free responses [J].

Wiskott, L .

NEURAL COMPUTATION, 2003, 15 (09) :2147-2177

[25] Slow feature analysis: Unsupervised learning of invariances [J].

Wiskott, L ;

Sejnowski, TJ .

NEURAL COMPUTATION, 2002, 14 (04) :715-770

[26]

ZIBULEVSKY M, 2000, P 2 INT WORKSH IND C, P489

[27]

ZURMUHL A, 1997, MATRIZEN ANWENDUNGEN, V1

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