A novel fixed-point algorithm for constrained independent component analysis

Constrained independent component analysis (ICA) is an effective method for solving the blind source separation with a prior knowledge. However, most constrained ICA algorithms are proposed for the real-valued sources. In this paper, a novel constrained noncircular complex fast independent component...

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Vydáno v:EURASIP journal on advances in signal processing Ročník 2019; číslo 1; s. 1 - 12
Hlavní autoři: Qian, Guobing, Wang, Lidan, Wang, Shiyuan, Duan, Shukai
Médium: Journal Article
Jazyk:angličtina
Vydáno: Cham Springer International Publishing 10.05.2019
Springer
Springer Nature B.V
SpringerOpen
Témata:
Algorithms
Comparative analysis
Complex
Computer simulation
Constrained ICA
Engineering
Fixed-point
Independent component analysis
Machine learning
Noncircular
Nonlinear programming
Quantum Information Technology
Quasi Newton methods
Separation
Signal processing
Signal,Image and Speech Processing
Spintronics
Constrained ICA
Fixed-point
Noncircular
Complex
ISSN:1687-6180, 1687-6172, 1687-6180
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Shrnutí:Constrained independent component analysis (ICA) is an effective method for solving the blind source separation with a prior knowledge. However, most constrained ICA algorithms are proposed for the real-valued sources. In this paper, a novel constrained noncircular complex fast independent component analysis (c-ncFastICA) algorithm based on the fixed-point learning is proposed to address the complex-valued sources. The c-ncFastICA algorithm uses the augmented Lagrangian method to obtain a new cost function and then utilizes the quasi-Newton method to search its optimal solution. Compared with other ICA and constrained ICA algorithms, c-ncFastICA has better separation performance. Simulations confirm the effectiveness and superiority of the c-ncFastICA algorithm.
Bibliografie:ObjectType-Article-1
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ISSN:1687-6180
1687-6172
1687-6180
DOI:10.1186/s13634-019-0622-8