dcTensor: An R package for discrete matrix/tensor decomposition

Abstract

Matrix factorization (MF) is a widely used approach to extract significant patterns in a data matrix. MF is formalized as the approximation of a data matrix X by the matrix product of two factor matrices U and V. Because this formalization has a large number of degrees of freedom, some constraints are imposed on the solution. Non-negative matrix factorization (NMF) imposing a non-negative solution for the factor matrices is a widely used algorithm to decompose non-negative matrix data matrix. Due to the interpretability of its non-negativity and the convenience of using decomposition results as clustering, there are many applications of NMF in image processing, audio processing, and bioinformatics (Cichocki et al., 2009).