Stochastic models in artificial intelligence development

Authors

  • Oksana L. Kyrychenko Yuriy Fedkovych Chernivtsi National University, 58012, Chernivtsi, Kotsiubynsky str, 2 https://orcid.org/0000-0003-0282-9958
  • Igor V. Malyk Yuriy Fedkovych Chernivtsi National University, 58012, Chernivtsi, Kotsiubynsky str, 2 https://orcid.org/0000-0002-1291-9167
  • Sergey E. Ostapov Yuriy Fedkovych Chernivtsi National University, 58012, Chernivtsi, Kotsiubynsky str, 2

DOI:

https://doi.org/10.17721/1812-5409.2021/2.7

Keywords:

stochastic random matrix, spectrum of a matrix, optimal number of clusters

Abstract

In this paper, we consider some properties of stochastic random matrices of large dimensions under conditions of independence of matrix elements or under conditions of independence of rows (columns). The main properties of stochastic random matrices spectrum are analyzed and the result of convergence to 0 is proved of almost all eigenvalues. Also, the application of these results to clustering problems and selection of the optimal number of clusters is considered. Note that the results obtained in this work are consistent with the Marchenko - Pastur theorem on the asymptotic distribution of eigenvalues of random matrices with independent elements. The results proved in this paper can be interpreted as a law of large numbers and will be used in the study of the asymptotic behavior of the maximum.

Pages of the article in the issue: 53 - 57

Language of the article: Ukrainian

References

ANDREW Y. NG, MICHAEL JORDAN, AND YAIR WEISS. (2002) On spectral clustering: Analysis and an algorithm, in NIPS, (2002).

FRANK LIN AND WILLIAM W. COHEN. (2010) Power iteration clustering, in ICML(to appear), (2010).

ZHIDONG BAI, ZHAOBEN FANG, YINGCHANG LIANG (2014). Spectral Theory of Large Dimensional Random Matrices and Its Applications to Wireless Communications and Finance Statistics : Random Matrix Theory and Its Applications. University of Science and Technology of China Press, World Scientific.

ROBERT C. QIU, PAUL ANTONIK (2017). Smart Grid using Big Data Analytics. A Random Matrix Theory Approach. Wiley Online Library, 2017.

KYRYCHENKO O.L. Provedennia optymalnoi klasteryzatsii struktury vebprostoru [Tekst] / O.L. Kyrychenko, S.E. Ostapov, I.Ia. Kanovskyi // Mizhnarodna naukovo-praktychna konferentsiia «Problemy informatyky ta kompiuternoi tekhniky» (PIKT-2017, 05-08 zhovtnia). Pratsi konferentsii. – Chernivtsi: Vydavnychyi dim «Rodovid», 2017. – Pp. 67-69.

KYRYCHENKO O.L. Zastosuvannia metodu kcore decomposition dlia provedennia optymalnoi klasteryzatsii / O.L. Kyrychenko, S.E. Ostapov Informatsiini tekhnolohii: nauka, tekhnika, tekhnolohiia, osvita, zdorovia: tezy dopovidei KhXVII mizhnarodnoi naukovo-praktychnoi konferentsii MicroCAD-2019, 15-17 travnia 2019 r.: u 4 ch. Ch. IV. / za red. prof. Sokola Ye.I. – Kharkiv: NTU «KhPI». – pp. 155

SHENG-TZONG CHENG, YIN-CHUN CHEN, AND MENG-SHUAN TSAI. (2017) Using k-Core Decomposition to Find Cluster Centers for k-Means Algorithm in GraphX on Spark, CLOUD COMPUTING 2017: The Eighth International Conference on Cloud Computing, GRIDs, and Virtualization.

KURARIA, AMIT & JHARBADE, NITIN & SONI, MANISH. (2018). Centroid Selection Process Using WCSS and Elbow Method for K-Mean Clustering Algorithm in Data Mining. International Journal of Scientific Research in Science, Engineering and Technology. Pp. 190-195. 10.32628/IJSRSET21841122.

V. A. MARCHENKO, L. A. PASTUR, Raspredelenye sobstvennykh znachenyi v nekotorykh ansambliakh sluchainykh matryts, Matem. sb., 1967, tom 72(114), nomer 4, Pp. 507–536.

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Published

2021-11-04

Issue

Section

Algebra, Geometry and Probability Theory

How to Cite

Kyrychenko, O. L., Malyk, I. V., & Ostapov, S. E. (2021). Stochastic models in artificial intelligence development. Bulletin of Taras Shevchenko National University of Kyiv. Physics and Mathematics, 2, 53-57. https://doi.org/10.17721/1812-5409.2021/2.7