Eigenvalue and Singular Value Decomposition in Linear Algebra and Data Analysis

20260 citationsJournal Articlehybrid Open Access

Authors

Aswathy P · Nagahama Institute of Bio-Science and Technology

Abstract

Eigenvalue Decomposition applies primarily to symmetric square matrices, Singular Value Decomposition extends these ideas to general matrices of any size. Together, they form the mathematical foundation for many modern applications in data analysis, machine learning, and scientific computing.

Topics & Keywords

Matrix Theory and Algorithms Tensor decomposition and applications Polynomial and algebraic computation

Publication Details

Published in: International Journal For Multidisciplinary Research

Volume 8, Issue 1

DOI: 10.36948/ijfmr.2026.v08i01.67855

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Eigenvalue and Singular Value Decomposition in Linear Algebra and Data Analysis

Authors

Abstract

Topics & Keywords

Publication Details