Adversarially-Trained Nonnegative Matrix Factorization
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Abstract (arXiv)
We consider an adversarially-trained version of the nonnegative matrix factorization, a popular latent dimensionality reduction technique. In our formulation, an attacker adds an arbitrary matrix of bounded norm to the given data matrix. We design efficient algorithms inspired by adversarial training to optimize for dictionary and coefficient matrices with enhanced generalization abilities. Extensive simulations on synthetic and benchmark datasets demonstrate the superior predictive performance on matrix completion tasks of our proposed method compared to state-of-the-art competitors, including other variants of adversarial nonnegative matrix factorization.
Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 10 of its 22 references that are in the map. First posted on arXiv 10 Apr 2021.
Builds on
10 of 22 references.
References · 10
- 2014Explaining and Harnessing Adversarial Examplescited by 7,287
- 2017Towards Deep Learning Models Resistant to Adversarial Attackscited by 6,326
- 2017Ensemble Adversarial Training: Attacks and Defensescited by 833
- 2017Certifying Some Distributional Robustness with Principled Adversarial Trainingcited by 333
- 2018Generalizable Adversarial Training via Spectral Normalizationcited by 68
- 2014Incremental Majorization-Minimization Optimization with Application to Large-Scale Machine Learningcited by 35
- 2014On the folded normal distributioncited by 31
- 2014Nonlinear hyperspectral unmixing with robust nonnegative matrix factorizationcited by 16
- 2016Online Nonnegative Matrix Factorization with Outlierscited by 7
- 2019Distributionally Robust and Multi-Objective Nonnegative Matrix Factorizationcited by 5
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Citing papers per year: 2023: 1; 2024: 1.
2 papers.