Matrix decomposition is the process of splitting a matrix into parts. It is also known as matrix factorization. There are lots of matrix decomposition methods available such as lower-upper (LU) decomposition, QR decomposition (where Q is orthogonal and R is upper-triangular), Cholesky decomposition, and SVD.
Eigenanalysis decomposes a matrix into vectors and values. SVD decomposes a matrix into the following parts: singular vectors and singular values. SVD is widely used in signal processing, computer vision, natural language processing (NLP), and machine learning—for example, topic modeling and recommender systems where SVD is widely accepted and implemented in real-life business solutions. Have a look at the following:
Here, A is a m x n left singular matrix, Σ is a n x n diagonal matrix, V is a m x n right singular matrix, and VT is the transpose of the V. The numpy.linalg subpackage...
