Work

Objects features extraction by singular projections of data tensor to matrices

Year: 2025

Type: article

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.

Source: Informatyka Automatyka Pomiary w Gospodarce i Ochronie Środowiska

DOI: 10.35784/iapgos.6912

Authors: Yuriy Bunyak, Roman Kvуetnyy, Olga Sofina, Volodymyr Kotsiubynskyi

Institution: Vinnytsia National Technical University

Language: English

Topic: Tensor decomposition and applications

Subfield: Computational Mathematics

Field: Mathematics

Domain: Physical Sciences

Open Access status: diamond

OpenAlex ID: W4414848507

FWCI:

Cites: 8

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