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.
Authors
- Perttu Saarela
- Una Radojičić (ORCID: https://orcid.org/0000-0003-0329-0595)
- Klaus Nordhausen (ORCID: https://orcid.org/0000-0002-3758-8501)
- Sara Taskinen (ORCID: https://orcid.org/0000-0001-9470-7258)
Institutions
- University of Helsinki (FI)
- TU Wien (AT)
- University of Jyväskylä (FI)
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
Funders
- Helsingin Yliopisto
- China Scholarship Council