Singular value decomposition (SVD) is a linear algebra technique where a matrix is factored into product of three matrices, that is A = UΣVT. Σ is a diagonal matrix and its entries are called singular values. Interestingly for an image, only the top few singular values contains most of the "information" to represent the image. For further information refer: https://en.wikipedia.org/wiki/Singular_value_decomposition
I'm M.Sc student in Computer Science at Tehran-Polytechnic (AUT) and interested research in Machine Learning ,Natural Language Processing and Data Science.
