伴随着国内外对盲源分离问题研究的日益深入,在独立分量分析等经典算法之外逐步发展出了许多新的算法。稀疏分量分析就是其中有效的方法之一,它利用信号的稀疏分解,克服了独立分量分析非欠定性的要求,解决了欠定情况下的盲源分离问题。本文将以稀疏分量分析为主要对象,归纳总结了近期的研究进展。
With the development of blind source separation ( BSS), various algorithms have been proposed except the classic algorithms such as independent component analysis (ICA). Sparse component analysis (SCA) based on sparse representation is one of the newly developed algorithms. SCA solves the problem of underdetermined BSS that ICA doesn' t solve. This paper presents a survey and review focusing on the study and development of SCA.