Adversarial Nonnegative Matrix Factorization
Authors: Lei Luo, Yanfu Zhang, Heng Huang
ICML 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments verify that ANMF is robust to a broad categories of perturbations, and achieves state-of-the-art performances on distinct real-world benchmark datasets. |
| Researcher Affiliation | Collaboration | 1JD Finance America Corporation, Mountain View, CA, USA 2Department of Electrical and Computer Engineering, University of Pittsburgh, PA, USA. |
| Pseudocode | Yes | Algorithm 1 Solving Eq. (9) via ADMM |
| Open Source Code | No | The paper does not provide an explicit statement about releasing the source code or a direct link to a code repository for the methodology described. |
| Open Datasets | Yes | Table 1. Description of Benchmark Datasets Dataset Number of Instances Dimensions Classes Category MNIST 150 784 10 image Yale 165 1024 15 image ORL 400 644 40 image UMIST 575 644 20 image COIL-20 1440 1024 20 image USPS 9298 256 10 image BBCsports 737 4613 5 text BBCNews 2225 9635 5 text Web KB 4199 7770 4 text Reuters 9298 256 10 text RCV 9625 29992 4 text TDT2 9394 36771 30 text |
| Dataset Splits | No | The paper does not provide specific details on training, validation, and test dataset splits (e.g., percentages or sample counts). |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for experiments (e.g., CPU, GPU models, memory). |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers needed to replicate the experiment. |
| Experiment Setup | Yes | Throughout the experiments, we set ANMF parameters as α = 0.6, β = 10 5, γ = 10 3, λ = 10 3, and µ = 1. |