Comment on 'TVOR: Finding Discrete Total Variation Outliers among Histograms'

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Recent paper 'TVOR: Finding Discrete Total Variation Outliers Among Histograms' introduces the Total Variation Outlier Recognizer (TVOR) method for identification of outliers among a given set of histograms. After providing a theoretical discussion of the method and verifying its success on synthetic and population census data, it applies the TVOR model to histograms of ages of Holocaust victims produced using United States Holocaust Memorial Museum data. It purports to identify the list of victims of the Jasenovac concentration camp as potentially suspicious. In this comment paper, we show that the TVOR model and its assumptions are grossly inapplicable to the considered dataset. When applied to the considered data, the model is biased in assigning a higher outlier score to histograms of larger sizes, the set of data points is extremely sparse around the point of interest, the dataset has not been reviewed to remove obvious data processing errors, and, contrary to the model requirements, the distributions of the victims' ages naturally vary significantly across victim lists.

Original languageEnglish (US)
Article number9442768
Pages (from-to)78586-78593
Number of pages8
JournalIEEE Access
StatePublished - 2021
Externally publishedYes


  • Anomaly detection
  • histogram
  • outlier detection
  • total variation

ASJC Scopus subject areas

  • General Engineering
  • General Computer Science
  • General Materials Science


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