کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
6939435 | 1449971 | 2018 | 30 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Independent component analysis by lp-norm optimization
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کلمات کلیدی
موضوعات مرتبط
مهندسی و علوم پایه
مهندسی کامپیوتر
چشم انداز کامپیوتر و تشخیص الگو
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چکیده انگلیسی
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(âβ|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-(pâ¯<â¯2) and sub-Gaussian (pâ¯>â¯2) cases and shows superior performance in separating independent sources than the state of the art algorithms for ICA computation.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Pattern Recognition - Volume 76, April 2018, Pages 752-760
Journal: Pattern Recognition - Volume 76, April 2018, Pages 752-760
نویسندگان
Sungheon Park, Nojun Kwak,