Adaptive Weighted Nonnegative Matrix Factorization for Robust Feature Representation

TL;DR

This paper introduces an adaptive weighted nonnegative matrix factorization method that enhances robustness against noise by assigning importance weights to data points, outperforming existing robust NMF techniques.

Contribution

It proposes two novel strategies—fuzzier weighted and entropy weighted regularized techniques—for robust NMF with simple iterative solutions.

Findings

More robust feature representations on noisy datasets

Outperforms existing robust NMF methods

Effective in real-world noisy data scenarios

Abstract

Nonnegative matrix factorization (NMF) has been widely used to dimensionality reduction in machine learning. However, the traditional NMF does not properly handle outliers, so that it is sensitive to noise. In order to improve the robustness of NMF, this paper proposes an adaptive weighted NMF, which introduces weights to emphasize the different importance of each data point, thus the algorithmic sensitivity to noisy data is decreased. It is very different from the existing robust NMFs that use a slow growth similarity measure. Specifically, two strategies are proposed to achieve this: fuzzier weighted technique and entropy weighted regularized technique, and both of them lead to an iterative solution with a simple form. Experimental results showed that new methods have more robust feature representation on several real datasets with noise than exsiting methods.