Minimum distance index for bss, generalization, interpretation and asymptotics

Niko Lietzén, Joni Virta, Klaus Nordhausen, Pauliina Ilmonen*

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

3 Citations (Scopus)
68 Downloads (Pure)


We consider complex valued linear blind source separation, where the signal dimension might be smaller than the dimension of the observable data vector. In order to measure the success of the signal separation, we propose an extension of the minimum distance index and establish its properties. Interpretations for the index are derived through connections to signal-to-noise ratios and correlations. The interpretations are novel also for the real valued original case. In addition, we consider the asymptotic behavior of the extended minimum distance index. This paper is an invited extended version of the paper presented at the CDAM 2019 conference.

Original languageEnglish
Pages (from-to)57-68
Number of pages12
JournalAustrian Journal of Statistics
Issue number4
Publication statusPublished - 14 Apr 2020
MoE publication typeA1 Journal article-refereed


  • Asymptotic properties
  • Blind source separation
  • Performance indices


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  • Dimension Reduction for Tensorial Data

    Virta, J.


    Project: Academy of Finland: Other research funding

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