Luo J, Hu B, Ling X T, Liu R W
Electronic Engineering Department, Fudan University, Shanghai 200433, China.
IEEE Trans Neural Netw. 1999;10(4):912-7. doi: 10.1109/72.774259.
Conventional blind signal separation algorithms do not adopt any asymmetric information of the input sources, thus the convergence point of a single output is always unpredictable. However, in most of the applications, we are usually interested in only one or two of the source signals and prior information is almost always available. In this paper, a principal independent component analysis (PICA) concept is proposed.We try to extract the objective independent component directly without separating all the signals. A cumulant-based globally convergent algorithm is presented and simulation results are given to show the hopeful applicability of the PICA ideas.
传统的盲信号分离算法不采用输入源的任何非对称信息，因此单个输出的收敛点总是不可预测的。然而，在大多数应用中，我们通常只对一两个源信号感兴趣，并且几乎总是可以获得先验信息。本文提出了一种主独立成分分析（PICA）概念。我们试图直接提取目标独立成分，而不分离所有信号。提出了一种基于累积量的全局收敛算法，并给出了仿真结果以表明PICA思想的有望适用性。
