In this video, we explain an important matrix factorization technique, which is called Singular Value Decomposition or SVD for short. The idea is that we decompose a given matrix as a product of three matrices: left singular vectors, singular values, and right singular vectors. We explain the properties of these matrices and the decaying behavior of the diagonal matrix of singular values. We also discuss how to compute SVD using NumPy in Python via numpy.linalg.svd. Furthermore, we explain the importance of singular value decomposition for information extraction and data compression, including the procedure to find low-rank approximations. This video also contains a step-by-step implementation by Dr. Data Science.
numpy.linalg.svd
#SingularValueDecomposition #SVD #MatrixFactorization