Blind source separation by nuclear norm minimization and local recoverability analysis

Takashi Tanaka, Cedric Langbort, Lalit K. Mestha, Alvaro E. Gil

Research output: Contribution to journalArticle

Abstract

We propose a new blind source separation (BSS) algorithm that is effective when Hankel matrices constructed from individual source signals are near low-rank and satisfy a certain near-orthogonality condition. Source separation is achieved by finding a nonsingular reverse-mixing operation that minimizes nuclear norms of Hankel matrices constructed from estimated source signals. The new formulation results in a non-convex optimization problem involving a reverse-mixing matrix. Preliminary analysis of local recoverability of source signals as well as few numerical simulations are presented in this letter.

Original languageEnglish (US)
Article number6517482
Pages (from-to)827-830
Number of pages4
JournalIEEE Signal Processing Letters
Volume20
Issue number8
DOIs
StatePublished - Jul 17 2013

Keywords

  • Blind source separation
  • independent component analysis
  • nuclear norm
  • trace heuristics

ASJC Scopus subject areas

  • Signal Processing
  • Electrical and Electronic Engineering
  • Applied Mathematics

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