Probabilistic matrix clustering with feature priors for unbiased control selection

Dmitrii A. Usoltsev

2025 , VOLUME 25, NUMBER 5 ( september-october )

ISSN 2226-1494 (print), ISSN 2500-0373 (online)

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Editor-in-Chief

Nikiforov
Vladimir O.
D.Sc., Prof.

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doi: 10.17586/2226-1494-2025-25-5-999-1001

Probabilistic matrix clustering with feature priors for unbiased control selection

D. A. Usoltsev

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Article in Russian

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Usoltsev D.A. Probabilistic matrix clustering with feature priors for unbiased control selection. Scientific and Technical Journal of Information Technologies, Mechanics and Optics, 2025, vol. 25, no. 5, pp. 999–1001 (in Russian). doi: 10.17586/2226-1494-2025-25-5-999-1001

Abstract

We propose a probabilistic matrix-clustering method that leverages a prior distribution of features and dimensionality reduction (Singular Value Decomposition, SVD). The approach identifies, within a large control pool, a cluster statistically comparable to the test cohort, thereby reducing systematic bias in downstream comparative analyses. We show that the method correctly selects control groups in scenarios where standard nearest-neighbor matching produces false positives. The method has been used to construct control groups in studies based on the Russian Biobank at the Almazov National Medical Research Centre (Ministry of Health of the Russian Federation).

Keywords: matrix clustering, SVD, prior (feature) distribution, Mahalanobis distance, χ²-criterion

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