Hierarchical independent component analysis: A multi-resolution non-orthogonal data-driven basis

2016 ◽  
Vol 95 ◽  
pp. 133-149 ◽  
Author(s):  
Piercesare Secchi ◽  
Simone Vantini ◽  
Paolo Zanini
Keyword(s):  
Independent Component Analysis ◽  
Component Analysis ◽  
Independent Component ◽  
Data Driven

scholarly journals Data-driven re-referencing of intracranial EEG based on independent component analysis (ICA)

2018 ◽  
Vol 307 ◽  
pp. 125-137 ◽  
Author(s):  
Sebastian Michelmann ◽  
Matthias S. Treder ◽  
Benjamin Griffiths ◽  
Casper Kerrén ◽  
Frédéric Roux ◽  
...  
Keyword(s):  
Independent Component Analysis ◽  
Component Analysis ◽  
Independent Component ◽  
Data Driven ◽  
Intracranial Eeg

Independent component analysis based on data-driven reconstruction of multi-fault diagnosis

2017 ◽  
Vol 31 (12) ◽  
pp. e2932 ◽  
Author(s):  
Lin Feng ◽  
Yingwei Zhang ◽  
Xuguang Li ◽  
Yuanjian Fu
Keyword(s):  
Fault Diagnosis ◽  
Independent Component Analysis ◽  
Component Analysis ◽  
Independent Component ◽  
Data Driven

Data-driven tensor independent component analysis for model-based connectivity neurofeedback

NeuroImage ◽  
2019 ◽  
Vol 184 ◽  
pp. 214-226 ◽  
Author(s):  
Yury Koush ◽  
Nemanja Masala ◽  
Frank Scharnowski ◽  
Dimitri Van De Ville
Keyword(s):  
Independent Component Analysis ◽  
Component Analysis ◽  
Independent Component ◽  
Data Driven ◽  
Model Based

Sparse code shrinkage for signal enhancement of seismic data

Geophysics ◽  
2011 ◽  
Vol 76 (6) ◽  
pp. V151-V167 ◽  
Author(s):  
Charlotte Sanchis ◽  
Alfred Hanssen
Keyword(s):  
Independent Component Analysis ◽  
Sparse Coding ◽  
Seismic Data ◽  
Component Analysis ◽  
Independent Component ◽  
Sparse Code ◽  
Data Driven ◽  
Alternative Methods ◽  
Gaussian Kernel ◽  
Gaussian Density

Sparse code shrinkage is a method that is commonly used for image denoising and that has recently found some applications in seismic for random noise attenuation and multiple removal in a simplified form. Sparse coding finds a representation of the data in which each component is only rarely significantly active. Such a representation is closely related to independent component analysis. We discuss the link between sparse coding and independent component analysis, and show how the application of shrinkages to sparse components manages to attenuate the noise in seismic data. The use of data-driven shrinkages estimated from noise-free data is a necessary condition for this method to be efficient. We propose a realization of the data, attenuated in noise, that allows the derivation of data-driven shrinkage functions. They are obtained either by fitting this sparse representation with a given density model, or by estimating the density directly from the data. Two parametric models of density are investigated, the sparse density and the normal inverse Gaussian density, while the Gaussian kernel estimator estimates the density from the data. They are tested on both synthetic and real marine seismic data, and compared with f-x deconvolution and local SVD as alternative denoising methods. We show that the NIG density yields the best results, but the sparse density and the nonparametric density estimate are acceptable choices as well. Finally, the comparison with alternative methods shows that sparse code shrinkage is a credible alternative for the denoising of seismic data.

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