Nonnegative Low Rank Matrix Approximation for Nonnegative Matrices

TL;DR

This paper introduces a novel Nonnegative Low Rank Matrix (NLRM) approximation algorithm that outperforms traditional NMF in accuracy and provides valuable singular value insights, with applications demonstrated through experiments.

Contribution

The paper proposes a new NLRM approach that achieves smaller approximation errors and offers automatic singular value decomposition, unlike classical NMF.

Findings

NLRM achieves smaller approximation distances than NMF.

NLRM provides automatic singular value decomposition.

Experimental results confirm the advantages of NLRM over NMF.

Abstract

This paper describes a new algorithm for computing Nonnegative Low Rank Matrix (NLRM) approximation for nonnegative matrices. Our approach is completely different from classical nonnegative matrix factorization (NMF) which has been studied for more than twenty five years. For a given nonnegative matrix, the usual NMF approach is to determine two nonnegative low rank matrices such that the distance between their product and the given nonnegative matrix is as small as possible. However, the proposed NLRM approach is to determine a nonnegative low rank matrix such that the distance between such matrix and the given nonnegative matrix is as small as possible. There are two advantages. (i) The minimized distance by the proposed NLRM method can be smaller than that by the NMF method, and it implies that the proposed NLRM method can obtain a better low rank matrix approximation. (ii) Our low rank matrix admits a matrix singular value decomposition automatically which provides a significant index based on singular values that can be used to identify important singular basis vectors, while this information cannot be obtained in the classical NMF. The proposed NLRM approximation algorithm was derived using the alternating projection on the low rank matrix manifold and the non-negativity property. Experimental results are presented to demonstrate the above mentioned advantages of the proposed NLRM method compared the NMF method.