Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
409262 | Neurocomputing | 2008 | 10 Pages |
Abstract
Following the seminal work of Stone [Independent Component Analysis, The MIT Press, Cambridge, 2004], this paper presents a new metric for blind source separation (BSS). It is proved that the metric value of any linear combination of source signals is less than the largest one of sources under a loose condition. Further, the global optimization of this new metric is achieved by formulating it as a generalized eigenvalue (GE) problem. Subsequently, we give out a fast BSS algorithm. Moreover, we analyze the solution properties of ill-posed BSS, and further show that the proposed algorithm is applicable to such a case as well. The numerical simulations demonstrate the efficacy of our algorithm.
Keywords
Related Topics
Physical Sciences and Engineering
Computer Science
Artificial Intelligence
Authors
Hai-lin Liu, Yiu-ming Cheung,