Singular Value Decomposition

Singular value decomposition (SVD) is a powerful tool for data analysis. In terms of mathematics, it is a matrix decomposition with good numerical stability and guaranteed existence. It provides a tool for dimensionality reduction, i.e. identification of patterns in high-dimensional data stored in a single matrix and filtering out noise. SVD is closely related to a statistical method called principal component analysis (PCA) [1], the terms SVD and PCA are sometimes used interchangeably.