Earticle

초록

영어

Traditionally, the key idea of estimating independent component analysis (ICA) model is to maximize the non-Gaussianity, however, often with the assumption that density of data is near the standardized Gaussian density. To avoid the unsuitable assumption, this article uses the nonparametric density estimating method. A nonparametric independent component analysis algorithm based on Epanechnikov kernel function is proposed in this paper. This algorithm uses the Epanechnikov kernel estimator to estimate random variable distribution, meanwhile, employs the hypothesis test to derive the nonparametric likelihood ratio (NLR) objective function. For optimizing the nonparametric density estimation, the selection of kernel function and bandwidth is crucial. From the perspective of minimizing the mean integrated square error (MISE), this paper discusses the optimal selection and conducts experiments for further study. To increase the algorithmic convergence rate and reduce the running time, the quasi-newton method has been used to optimize the objective function. Compared with previous nonparametric ICA algorithm, the simulation results demonstrate that the proposed method offers better performance both on speech separation and computing capability.

목차

Abstract
 1. Introduction
 2. NLR-ICA Model Based on Epanechnikov Kernel Function
  2.1. ICA Model and Separation Principle
  2.2. The Selection of Kernel Function and Bandwidth
  2.3. Objective Function Derivation
 3. Simulation Experiments
 4. Conclusion
 Acknowledgements
 References

키워드

저자

• Liang-ju Yu [ Department of Automation and Key Laboratory of System Control and Information Processing, Shanghai Jiao Tong University, China ]
• Gen-ke Yang [ Department of Automation and Key Laboratory of System Control and Information Processing, Shanghai Jiao Tong University, China ]
• Yue Chen [ Department of Automation and Key Laboratory of System Control and Information Processing, Shanghai Jiao Tong University, China ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Control and Automation

• 간기

  월간

• pISSN

  2005-4297

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605