Review on matrix pseudo-inverse using singular value decomposition-SVD and application to regression

Abstract

Singular Value Decomposition (SVD) is one of the most factorization of the real or complex mathematical matrix problems. In this paper, one of the most significant applications of the Signa gular Value Decomposition (SVD) which is the Matrix decomposition is being selected to be described and explained as a regression model. The experimental results show that the SVD regression using Matrix-Pseudo Inverse results are more realistic and nearly as expected that the simple regression model when the results have been compared between the simple regression model and the SVD regression model based on the Matrix-Pseudo Inverse model based on implement them on the same dataset (data points). In this paper, two main cases are discussed. The first one is the insertable matrix pseudo-inverse, and the non-invertible matrix pseudo-inverse. Both cases are mainly discussed with a relative example given which shows that main approach that is used to compute based on the Singular Value Decomposition.</p