A learning framework for blind source separation using generalized Eigenvalues

Hailin Liu, Yiu Ming CHEUNG

Research output: Contribution to journalConference articlepeer-review

3 Citations (Scopus)

Abstract

This paper presents a learning framework for blind source separation (BSS), in which the BSS is formulated as generalized Eigenvalue (GE) problem. Compared to the typical information-theoretical approaches, this new one has at least two merits: (1) the unknown unmixing matrix directly works out from the GE equation without time-consuming iterative learning; (2) The correctness of the solution is guaranteed. We give out a general learning procedure under this framework. The computer simulation shows validity of our method.

Original languageEnglish
Pages (from-to)472-477
Number of pages6
JournalLecture Notes in Computer Science
Volume3497
Issue numberII
DOIs
Publication statusPublished - 2005
EventSecond International Symposium on Neural Networks: Advances in Neural Networks - ISNN 2005 - Chongqing, China
Duration: 30 May 20051 Jun 2005

Scopus Subject Areas

  • Theoretical Computer Science
  • Computer Science(all)

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