Robust blind source separation by beta divergence

Minami Mihoko, Shinto Eguchi

研究成果: Article査読

135 被引用数 (Scopus)

抄録

Blind source separation is aimed at recovering original independent signals when their linear mixtures are observed. Various methods for estimating a recovering matrix have been proposed and applied to data in many fields, such as biological signal processing, communication engineering, and financial market data analysis. One problem these methods have is that they are often too sensitive to outliers, and the existence of a few outliers might change the estimate drastically. In this article, we propose a robust method of blind source separation based on the α divergence. Shift parameters are explicitly included in our model instead of the conventional way which assumes that original signals have zero mean. The estimator gives smaller weights to possible outliers so that their influence on the estimate is weakened. Simulation results show that the proposed estimator significantly improves the performance over the existing methods when outliers exist; it keeps equal performance otherwise.

本文言語English
ページ(範囲)1859-1886
ページ数28
ジャーナルNeural Computation
14
8
DOI
出版ステータスPublished - 2002 8月
外部発表はい

ASJC Scopus subject areas

  • 人文科学(その他)
  • 認知神経科学

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