Extracting a Desired Independent Source in ICA

摘要：

We propose a novel approach to extract a detired independent source from multidimensional observations when a priori information of the source is available. An extension to the constrained independent component analysis (cICA) is adopted to model such extraction. A solution to the constrained optimization problem with a Newton-like learning algorithm is provided. The convergence of the learning procedure is proved and analyzed. Simulations of extracting a single source from Gaussian-class signals and an activation time response from synthetic fMRI data demonstrate the efficacy and accuracy of our algorithm compared to other methods.

关键词： constrained independent component analysis one-unit ICA ICA with reference

作者: Wei Lu Jagath C. Rajapakse

作者单位: School of Computer Engineering Nanyang Technological University, Singapore

会议类型: 国际会议

会议名称: 8th International Conference on Neural Information Processing(ICONIP 2001)(第八届国际神经信息处理大会)

会议地点: 上海

会议语种:英文

页码: 839-844

在线出版日期: 2001-11-14（万方平台首次上网日期，不代表论文的发表时间）