Using Singular Value Decomposition to Reduce Dimensionality of Initial Data Set

被引：2

作者：

Uzhga-Rebrov, Oleg ^{[1
]}

Kuleshova, Galina ^{[2
]}

机构：

[1] Rezekne Acad Technol, Informat & Commun Technol Res Ctr, Rezekne, Latvia

[2] Riga Tech Univ, Inst Informat Technol, Riga, Latvia

来源：

2020 61ST INTERNATIONAL SCIENTIFIC CONFERENCE ON INFORMATION TECHNOLOGY AND MANAGEMENT SCIENCE OF RIGA TECHNICAL UNIVERSITY (ITMS) | 2020年

关键词：

left eigenvectors; matrix rank; right eigenvectors; singular value decomposition; singular value matrix;

D O I：

10.1109/itms51158.2020.9259304

中图分类号：

TP39 [计算机的应用];

学科分类号：

081203 ; 0835 ;

摘要：

The purpose of any data analysis is to extract essential information implicitly present in the data. To do this, it often seems necessary to transform the initial data into a form that allows one to identify and interpret the essential features of their structure. One of the most important tasks of data analysis is to reduce the dimension of the original data. The paper considers an approach to solving this problem based on singular value decomposition (SVD).

引用

页数：4

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