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

Plain Language 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.

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Publication Details

Journal
Advances in Data Analysis and Classification
Published
2026-06-25
DOI
https://doi.org/10.1007/s11634-026-00694-5
Primary Topic
Blind Source Separation Techniques
Type
article
Field-Weighted Citation Impact
0.00

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

Perttu Saarela, Una Radojičić, Klaus Nordhausen, Sara Taskinen
Advances in Data Analysis and Classification
Blind Source Separation Techniques
article

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

Perttu Saarela, Una Radojičić, Klaus Nordhausen, Sara Taskinen
article en

Abstract

Plain Language 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.

Advances in Data Analysis and Classification
University of Helsinki (FI), TU Wien (AT), University of Jyväskylä (FI)
Helsingin Yliopisto, China Scholarship Council
Openalex Percentile: Top 4%
Blind Source Separation Techniques
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