Pattern Recognition and Image Analysis

, Volume 28, Issue 4, pp 595–604 | Cite as

On Metric Correction and Conditionality of Raw Featureless Data in Machine Learning

  • S. D. DvoenkoEmail author
  • D. O. Pshenichny
Proceedings of the 6th International Workshop


Recently, raw experimental data in machine learning often appear as direct comparisons between objects (featureless data). Different ways to evaluate difference or similarity of a pair of objects in image and data mining, image analysis, bioinformatics, etc., are usually used in practice. Nevertheless, such comparisons often are not distances or correlations (scalar products) like a correct function defined on a limited set of elements in machine learning. This problem is denoted as metric violations in ill-posed matrices. Therefore, it needs to recover violated metrics and provide optimal conditionality of corresponding matrices of pairwise comparisons for distances and similarities. This is the correct basis for using of modern machine learning algorithms.


metrics similarity dissimilarity distance scalar product condition number determinant principal minor eigenvalue 


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Copyright information

© Pleiades Publishing, Ltd. 2018

Authors and Affiliations

  1. 1.Tula State UniversityTulaRussia

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