Efficient Preconditioning for Noisy Separable NMFs by Successive Projection Based Low-Rank Approximations

TL;DR

This paper introduces a computationally efficient modification to the preconditioning method for noisy separable nonnegative matrix factorization, enhancing robustness while reducing the cost of the original approach.

Contribution

It proposes a new preconditioning algorithm using SPA-based low-rank approximation, balancing accuracy and efficiency for noisy NMF problems.

Findings

The SPA-based approximation achieves comparable accuracy to SVD-based methods.

The modified preconditioner improves noise robustness in NMF.

Empirical results demonstrate reduced computational cost with maintained performance.

Abstract

The successive projection algorithm (SPA) can quickly solve a nonnegative matrix factorization problem under a separability assumption. Even if noise is added to the problem, SPA is robust as long as the perturbations caused by the noise are small. In particular, robustness against noise should be high when handling the problems arising from real applications. The preconditioner proposed by Gillis and Vavasis (2015) makes it possible to enhance the noise robustness of SPA. Meanwhile, an additional computational cost is required. The construction of the preconditioner contains a step to compute the top-$k$ truncated singular value decomposition of an input matrix. It is known that the decomposition provides the best rank-$k$ approximation to the input matrix; in other words, a matrix with the smallest approximation error among all matrices of rank less than $k$. This step is an obstacle to an efficient implementation of the preconditioned SPA. To address the cost issue, we propose a modification of the algorithm for constructing the preconditioner. Although the original algorithm uses the best rank-$k$ approximation, instead of it, our modification uses an alternative. Ideally, this alternative should have high approximation accuracy and low computational cost. To ensure this, our modification employs a rank-$k$ approximation produced by an SPA based algorithm. We analyze the accuracy of the approximation and evaluate the computational cost of the algorithm. We then present an empirical study revealing the actual performance of the SPA based rank-$k$ approximation algorithm and the modified preconditioned SPA.