Review of Matrix Decomposition Techniques for Signal Processing Applications

Decomposition of matrix is a vital part of many scientific and engineering applications. It is a technique that breaks down a square numeric matrix into two different square matrices and is a basis for efficiently solving a system of equations, which in turn is the basis for inverting a matrix. An inverting matrix is a part of many important algorithms. Matrix factorizations have wide applications in numerical linear algebra, in solving linear systems, computing inertia, and rank estimation is an important consideration. This paper presents review of all the matrix decomposition techniques used in signal processing applications on the basis of their computational complexity, advantages and disadvantages. Various Decomposition techniques such as LU Decomposition, QR decomposition , Cholesky decomposition are discussed here.