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Pattern Recognition and Image Analysis
Erschienen in: Pattern Recognition and Image Analysis 2/2020

01.04.2020 | SPECIAL ISSUE

Multivariate Scaling of the Characteristic Features Based on Pseudo-Inverse Operations for Recognition Problems Solving

verfasst von: Iu. V. Krak, V. S. Kasianiuk, H. I. Kudin, O. V. Barmak, E. A. Manziuk

Erschienen in: Pattern Recognition and Image Analysis | Ausgabe 2/2020

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Abstract

Some approach to multidimensional information scaling of the characteristics features based on results of theory of perturbation of pseudo-inverse and projective matrices and solutions of systems of linear algebraic equations is proposed. The method and algorithm of a piecewise hyperplane clusters creating with the verification of a given criterion for effectiveness of the proposed method of clustering is developed. The problem of stability of main indicators of the classifier in presence disturbances in source information is investigated. The proposed method for determining influence of source data errors on main indicators of the classifier provides presentation of undisturbed information matrix in form of splitting matrices of special kind. Advantages of the proposed approach are demonstrated, an example of using the method of scaling characteristic features for recognizing fingerspelling alphabet of sign language is given.
Vorheriger Artikel Models, Algorithms, and Architecture for Generating Adaptive Decision Support Systems
Nächster Artikel Graphics Pipeline Evolution Based on Object Shaders

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Metadaten
Titel
Multivariate Scaling of the Characteristic Features Based on Pseudo-Inverse Operations for Recognition Problems Solving
verfasst von
Iu. V. Krak
V. S. Kasianiuk
H. I. Kudin
O. V. Barmak
E. A. Manziuk
Publikationsdatum
01.04.2020
Verlag
Pleiades Publishing
Erschienen in
Pattern Recognition and Image Analysis / Ausgabe 2/2020
Print ISSN: 1054-6618
Elektronische ISSN: 1555-6212
DOI
https://doi.org/10.1134/S1054661820020078

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