SVD stands for Singular Value Decomposition, and it is a mathematical technique used in linear algebra. Singular Value Decomposition breaks down a matrix into three other matrices, revealing the underlying structure and patterns within the original matrix. It has applications in various fields, including signal processing, statistics, machine learning, and data analysis.
A=UΣV^T where: U is an orthogonal matrix containing the left singular vectors. Σ is a diagonal matrix containing the singular values. V^T is the transpose of an orthogonal matrix containing the right singular vectors.