Browse

Independent Component Analysis by lp-norm Optimization

Cited 5 time in Web of Science Cited 4 time in Scopus
Authors
Park, Sungheon; Kwak, Nojun
Issue Date
2017-10
Publisher
ELSEVIER SCI LTD
Citation
Pattern Recognition, Vol.76, pp. 752-760
Keywords
Independent Component Analysis by lp-norm Optimization자연과학ICAPCAlp-NormMaximum likelihood estimationSuper-GaussianSub-Gaussian
Abstract
In this paper, a couple of new algorithms for independent component analysis (ICA) are proposed. In the proposed methods, the independent sources are assumed to follow a predefined distribution of the form f(s)=αexp(−β|s|p) and a maximum likelihood estimation is used to separate the sources. In the first method, a gradient ascent method is used for the maximum likelihood estimation, while in the second, a non-iterative algorithm is proposed based on the relaxation of the problem. The maximization of the log-likelihood of the estimated source XTw given the parameter p and the data X is shown to be equivalent to the minimization of lp-norm of the projected data XTw. This formulation of ICA has a very close relationship with the Lp-PCA where the maximization of the same objective function is solved. The proposed algorithm solves an approximation of the lp-norm minimization problem for both super-(p < 2) and sub-Gaussian (p > 2) cases and shows superior performance in separating independent sources than the state of the art algorithms for ICA computation.
ISSN
0031-3203
Language
English
URI
http://hdl.handle.net/10371/139292
DOI
https://doi.org/10.1016/j.patcog.2017.10.006
Files in This Item:
There are no files associated with this item.
Appears in Collections:
Graduate School of Convergence Science and Technology (융합과학기술대학원)Dept. of Transdisciplinary Studies(융합과학부)Journal Papers (저널논문_융합과학부)
  • mendeley

Items in S-Space are protected by copyright, with all rights reserved, unless otherwise indicated.

Browse