On blind source separation using generalized eigenvalues with a new metric

Hai lin Liu, Yiu Ming CHEUNG*

*Corresponding author for this work

Research output: Contribution to journalJournal articlepeer-review

6 Citations (Scopus)


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.

Original languageEnglish
Pages (from-to)973-982
Number of pages10
Issue number4-6
Publication statusPublished - Jan 2008

Scopus Subject Areas

  • Computer Science Applications
  • Cognitive Neuroscience
  • Artificial Intelligence

User-Defined Keywords

  • Blind source separation
  • Generalized eigenvalue problem
  • Ill-posed ICA
  • Independent component analysis
  • Optimal solution


Dive into the research topics of 'On blind source separation using generalized eigenvalues with a new metric'. Together they form a unique fingerprint.

Cite this