Non-negative Matrix Factorization for Dimensionality Reduction

Abstract

Abstract—What matrix factorization methods do is reduce the dimensionality of the data without losing any important information. In this work, we present the Non-negative Matrix Factorization (NMF) method, focusing on its advantages concerning other methods of matrix factorization. We discuss the main optimization algorithms, used to solve the NMF problem, and their convergence. The paper also contains a comparative study between principal component analysis (PCA), independent component analysis (ICA), and NMF for dimensionality reduction using a face image database. Index Terms—NMF, PCA, ICA, dimensionality reduction.