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<p>In this discussion, we comment on the paper by Leyder et al., which introduces a new framework for robust independent component analysis based on robust distance correlation. Our discussion focuses on the role of whitening, the choice of scatter matrices, and the implications of the independence property in robust ICA. We further argue that the minimum distance index is preferable to the Amari index as a performance measure, as it admits a direct connection to the limiting distribution of ICA estimators. Overall, the paper by Leyder et al. constitutes an important contribution to robust blind source separation and opens several promising avenues for future research. </p>

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Discussion to the paper “Independent component analysis by robust distance correlation”

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年6月25日

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  • Klaus Nordhausen,
  • Una Radojičić,
  • Perttu Saarela,
  • Sara Taskinen

摘要

In this discussion, we comment on the paper by Leyder et al., which introduces a new framework for robust independent component analysis based on robust distance correlation. Our discussion focuses on the role of whitening, the choice of scatter matrices, and the implications of the independence property in robust ICA. We further argue that the minimum distance index is preferable to the Amari index as a performance measure, as it admits a direct connection to the limiting distribution of ICA estimators. Overall, the paper by Leyder et al. constitutes an important contribution to robust blind source separation and opens several promising avenues for future research.