Adaptive Weighted Nonnegative Matrix Factorization for Robust Feature Representation
This work addresses robustness in dimensionality reduction for machine learning applications, but it is incremental as it builds on existing NMF techniques.
The paper tackled the problem of nonnegative matrix factorization being sensitive to noise by proposing an adaptive weighted NMF that uses weights to reduce sensitivity to outliers, resulting in more robust feature representation on real datasets with noise compared to existing methods.
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.