The Target Polish: A New Approach to Outlier-Resistant Non-Negative Matrix Factorization
This work addresses a computational bottleneck for researchers and practitioners using robust NMF in applications like image processing, though it is incremental as it builds on existing Fast-HALS methods.
The paper tackled the problem of slow convergence in outlier-resistant Non-Negative Matrix Factorization by introducing the Target Polish framework, which achieved comparable or better accuracy than state-of-the-art methods while reducing computational time by an order of magnitude in image datasets with noise.
This paper introduces the "Target Polish," a robust and computationally efficient framework for Non-Negative Matrix Factorization (NMF). Although conventional weighted NMF approaches are resistant to outliers, they converge slowly due to the use of multiplicative updates to minimize the objective criterion. In contrast, the Target Polish approach remains compatible with the Fast-HALS algorithm, which is renowned for its speed, by adaptively "polishing" the data with a weighted median-based transformation. This innovation provides outlier resistance while maintaining the highly efficient additive update structure of Fast-HALS. Empirical evaluations using image datasets corrupted with structured (block) and unstructured (salt) noise demonstrate that the Target Polish approach matches or exceeds the accuracy of state-of-the-art robust NMF methods while reducing computational time by an order of magnitude in the studied scenarios.