Nonnegative independent component analysis based on minimizing mutual information technique

A novel neural network technique for nonnegative independent component analysis is proposed in this letter. Compared with other algorithms, this method can work efficiently even when the source signals are not well grounded. Moreover, this method is insensitive to the particular underlying distribution of the source data. Experimental results demonstrate the advantages of our approach in achieving satisfactory results regardless of whether the source data are well grounded or not. r 2005 Elsevier B.V. All rights reserved.

• Type: Journal
• Year: 2006
• Where: IJON
• Authors: Chun-Hou Zheng, De-Shuang Huang, Zhan-Li Sun, Michael R. Lyu, Tat-Ming Lok
• Keywords: IJON 2006, Nonnegative Independent Component, Particular Underlying Distribution, Source Data