Objects features extraction by singular projections of data tensor to matrices

Yuriy Bunyak; Roman Kvуetnyy; Olga Sofina; Volodymyr Kotsiubynskyi

doi:10.35784/iapgos.6912

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Published: 2025-09-30

DOI: https://doi.org/10.35784/iapgos.6912

Issue Vol. 15 No. 3 (2025)

Objects features extraction by singular projections of data tensor to matrices
Yuriy Bunyak, Roman Kvуetnyy, Olga Sofina, Volodymyr Kotsiubynskyi

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Authors

Yuriy Bunyak

iuriy.buniak@gmail.com

Spilna Sprava Company

https://orcid.org/0000-0002-0862-880X

Roman Kvуetnyy

rkvyetnyy@gmail.com

Vinnytsia National Technical University

https://orcid.org/0000-0002-9192-9258

Olga Sofina

olsofina@gmail.com

Vinnytsia National Technical University

Volodymyr Kotsiubynskyi

Vkotsyubinsky@gmail.com

Vinnytsia National Technical University

Abstract

The problem of multidimensional tensor objects features extraction in a manner of matrices is considered. The tensor’ elements Higher Order Singular Value Decomposition (SVD) is presented as the d-SVD which includes SVD of the tensor reshaped as a matrix and SVDs of reduced size of the previous SVDs vectors reshaped as matrices. The decomposition allows to create Singular Projections of tensor to a sum of one-rank tensors in selected dimensions. The projections of tensor to matrices by weighted and direct averaging in SVD’ vectors subspace is investigated numerically. The extracted by projection features of a series of image objects are used to develop the optimized Inverse Feature Filters for the objects recognition.

Keywords:

high order singular value decomposition, singular projection, objects recognition, optimized inverse features filters

References

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Objects features extraction by singular projections of data tensor to matrices. (2025). Informatyka, Automatyka, Pomiary W Gospodarce I Ochronie Środowiska, 15(3), 5-9. https://doi.org/10.35784/iapgos.6912

Objects features extraction by singular projections of data tensor to matrices

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