Intersecting Faces: Non-negative Matrix Factorization With New Guarantees
Authors: Rong Ge, James Zou
ICML 2015 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We explored the performance of Face-Intersect on simulations and discuss settings where it empirically outperformed the state-of-art methods. |
| Researcher Affiliation | Industry | Rong Ge RONGGE@MICROSOFT.COM Microsoft Research New England, James Zou JAZO@MICROSOFT.COM Microsoft Research New England |
| Pseudocode | Yes | Algorithm 1 Face-Intersect, Algorithm 2 Finding a properly filled facet, Algorithm 3 Finding all proper facets, Algorithm 4 Finding Intersection, Algorithm 5 Finding remaining vertices |
| Open Source Code | No | The paper does not provide any specific links or explicit statements indicating that open-source code for the described methodology is available. |
| Open Datasets | No | The paper states, "We simulated data according to the generative NMF model described in Section 7," and describes the data generation process, but it does not provide concrete access information (link, DOI, repository, or standard benchmark citation) for a publicly available or open dataset. |
| Dataset Splits | No | The paper describes simulating data and experimental settings but does not provide specific details on training, validation, or test dataset splits. |
| Hardware Specification | No | The paper does not explicitly describe the specific hardware (e.g., CPU/GPU models, memory) used to run its experiments. |
| Software Dependencies | No | The paper mentions algorithms like "Anchor-Words algorithm" and "Projected Gradient" but does not provide specific version numbers for any software dependencies or libraries. |
| Experiment Setup | Yes | We tested a range of settings with m between 5 to 100, r between 3 to 10, and n1 and n2 between 100 and 500. We generated the true data as M = AW and added i.i.d. Gaussian noise to each entry of M to generate the observed data M. |