Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Orthogonal NMF through Subspace Exploration
Authors: Megasthenis Asteris, Dimitris Papailiopoulos, Alexandros G. Dimakis
NeurIPS 2015 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate our algorithms on several real and synthetic datasets and show that their performance matches or outperforms the state of the art. |
| Researcher Affiliation | Academia | Megasthenis Asteris The University of Texas at Austin EMAIL Papailiopoulos University of California, Berkeley EMAIL G. Dimakis The University of Texas at Austin EMAIL |
| Pseudocode | Yes | Algorithm 1 Low Rank NNPCA Algorithm 2 ONMFS Algorithm 3 Local Opt W |
| Open Source Code | No | The paper does not provide any explicit statements or links indicating that the source code for the described methodology is publicly available. |
| Open Datasets | Yes | CBCL Dataset. The CBCL dataset [30] contains 2429, 19 19 pixel, gray scale face images. It has been used in the evaluation of all three methods [16, 17, 27]. Additional Datasets. We solve the NNPCA problem on various datasets obtained from [31]. |
| Dataset Splits | No | The paper does not provide specific details on dataset splits for training, validation, or testing, nor does it mention cross-validation setups. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware (e.g., CPU, GPU models, memory) used to run the experiments. |
| Software Dependencies | No | The paper mentions general tools like SVD, but it does not specify any software dependencies (e.g., libraries, frameworks) with version numbers. |
| Experiment Setup | Yes | For our algorithm, we use a sketch of rank r = 4 of the (centered) input data. Further we apply an early termination criterion; execution is terminated if no improvement is observed in a number of consecutive iterations (samples). We set a high penalty (α = 1e10) to promote orthogonality. We run ONMF methods with target dimension k = 5. For the methods that involved random initialization, we run 10 averaging iterations per Monte Carlo trial. We compare our algorithm with several state-of-the-art ONMF algorithms i) the O-PNMF algorithm of [13] (for 1000 iterations), and ii) the more recent ONP-MF iii) EM-ONMF algorithms of [11, 32] (for 1000 iterations). |