title: On extending the complex FastICA algorithms to noisy data.
abstract: Independent component analysis (ICA) methods are widely applied to modern digital signal processing. The complex-valued FastICA algorithms are one type of the most significant methods. However, the complex ICA model usually omits the noise. In this paper, we discuss two complex FastICA algorithms for noisy data, where the cost functions are based on kurtosis and negentropy respectively. The nc-FastICA and KM-F algorithms are modified to separate noisy data. At the same time, we also give the stability conditions of cost functions. Simulations are presented to illustrate the effectiveness of our methods.
authors.affiliation: School of Electronic Engineering, University of Electronics Science and Technology of China, Chengdu 611731, China; College of Science, China University of Petroleum, Qingdao 266580, China. Electronic address: ruanzl0496@gmail.com.
authors.forename: Zongli
authors.initials: Z
authors.lastname: Ruan
authors.affiliation: School of Electronic Engineering, University of Electronics Science and Technology of China, Chengdu 611731, China.
authors.forename: Liping
authors.initials: L
authors.lastname: Li
authors.affiliation: School of Electronic Engineering, University of Electronics Science and Technology of China, Chengdu 611731, China.
authors.forename: Guobing
authors.initials: G
authors.lastname: Qian
mesh_terms: D000465:Algorithms; D012815:Signal Processing, Computer-Assisted; D059629:Signal-To-Noise Ratio
keywords: Complex fast fixed-point algorithm; Independent component analysis (ICA); Noisy data; Pseudo-whitening; Stability condition