Adaptive Quasi-Newton Algorithm for Source Extraction via CCA Approach

作者：Zhang, Wei-Tao^*; Lou, Shun-Tian; Feng, Da-Zheng

来源：IEEE Transactions on Neural Networks and Learning Systems, 2014, 25(4): 677-689.

DOI：10.1109/TNNLS.2013.2280285

• Blind source extraction (BSE)
• canonical correlation analysis (CCA)
• Lyapunov method
• Newton iteration
• stability

This paper addresses the problem of adaptive source extraction via the canonical correlation analysis (CCA) approach. Based on Liu's analysis of CCA approach, we propose a new criterion for source extraction, which is proved to be equivalent to the CCA criterion. Then, a fast and efficient online algorithm using quasi-Newton iteration is developed. The stability of the algorithm is also analyzed using Lyapunov's method, which shows that the proposed algorithm asymptotically converges to the global minimum of the criterion. Simulation results are presented to prove our theoretical analysis and demonstrate the merits of the proposed algorithm in terms of convergence speed and successful rate for source extraction.

• 出版日期2014-4
• 单位西安电子科技大学