一种快速的多个主成分并行提取算法

A Fast Algorithm That Extracts Multiple Principle Components in Parallel

主成分分析是信号处理和数据统计领域内非常重要的分析工具.针对现有多个主成分提取算法收敛速度慢的问题,提出了具有快速收敛速度的神经网络算法.该算法能够并行提取信号中的多个主成分,而不需要其他额外的操作.分别采用平稳点分析法和随机离散时间分析法对所提算法的收敛性和自稳定性进行了证明.仿真实验表明,相比现有算法,所提算法不仅具有较快的收敛速度,而且具有较高的收敛精度.

Principle component analysis is a powerful tool in signal processing and data analysis. Up to now, some existing algorithms for multiple principal components extraction have a slow convergence speed. In order to solve this problem, a faster convergence algorithm is proposed, which can extract multiple principal components from the input signal in parallel. The equilibrium point analysis method and stochastic discrete time method are adopted to analyze the convergence and self-stabilizing property of the proposed algorithm. Simulation results show that compared with some existing algorithms, the proposed algorithm not only has a faster convergence speed but also has a higher estimating accuracy.