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Adversarially-Trained Nonnegative Matrix Factorization
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DOI：10.1109/LSP.2021.3092231.png)
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摘要
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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.
Keyword:
Optimization
Standards
Task analysis
Matrix decomposition
Dictionaries
Training
Signal processing algorithms
Adversarial training
non-negative matrix factorization
matrix completion
期刊
IF:
9.6
论文数:
1.1W
被引数:
1.7W
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