overcomplete representation
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2021 ◽  
Vol 0 (0) ◽  
pp. 0
Author(s):  
Monica Pragliola ◽  
Daniela Calvetti ◽  
Erkki Somersalo

2009 ◽  
Vol 56 (2) ◽  
pp. 200-204 ◽  
Author(s):  
M. Dyrholm ◽  
R. Goldman ◽  
P. Sajda ◽  
T.R. Brown

2008 ◽  
Vol 20 (3) ◽  
pp. 636-643 ◽  
Author(s):  
Zhaoshui He ◽  
Shengli Xie ◽  
Liqing Zhang ◽  
Andrzej Cichocki

Overcomplete representations have greater robustness in noise environment and also have greater flexibility in matching structure in the data. Lewicki and Sejnowski (2000) proposed an efficient extended natural gradient for learning the overcomplete basis and developed an overcomplete representation approach. However, they derived their gradient by many approximations, and their proof is very complicated. To give a stronger theoretical basis, we provide a brief and more rigorous mathematical proof for this gradient in this note. In addition, we propose a more robust constrained Lewicki-Sejnowski gradient.


2005 ◽  
Vol 118 (6) ◽  
pp. 3706-3720 ◽  
Author(s):  
Guang-Ming Zhang ◽  
David M. Harvey ◽  
Derek R. Braden

2005 ◽  
Vol 50 (23) ◽  
pp. 2672-2677 ◽  
Author(s):  
Zhang Chunmei ◽  
Yin Zhongke ◽  
Chen Xiangdong ◽  
Xiao Mingxia

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