Improved Local Search for Binary Matrix Factorization
Authors: Seyed Hamid Mirisaee, Eric Gaussier, Alexandre Termier
AAAI 2015 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | With thorough experiments on both synthetic and real data, we demonstrate that the heuristic we propose significantly improves the quality of existing methods in a reasonable amount of time. We also show that our local search procedure performs well when compared to other heuristic strategies (Kanungo et al. 2002). |
| Researcher Affiliation | Academia | Seyed Hamid Mirisaee Univ. Grenoble Alps/CNRS Grenoble, France Hamid.Mirisaee@imag.fr Eric Gaussier Univ. Grenoble Alps/CNRS Grenoble, France Eric.Gaussier@imag.fr Alexandre Termier Univ. Rennes I/CNRS Rennes, France Alexandre.Termier@irisa.fr |
| Pseudocode | No | The paper describes algorithmic procedures but does not include structured pseudocode or clearly labeled algorithm blocks. |
| Open Source Code | No | For SVD and NMF, we used the Matlab built-in functions and for the rest we have used our own efficient implementations (all the codes are available from the authors upon request). |
| Open Datasets | Yes | Following the methodology used in (Uno, Kiyomi, and Arimura 2005), we examined both real world datasets and synthetic ones, all available at http://fimi.ua.ac.be/data/ (last visited 15-Nov-2014). |
| Dataset Splits | No | The paper does not provide specific dataset split information (exact percentages, sample counts, citations to predefined splits, or detailed splitting methodology) needed to reproduce the data partitioning. |
| Hardware Specification | No | The paper does not explicitly describe the hardware used to run its experiments, such as specific GPU/CPU models, processors, or cloud resources with specifications. |
| Software Dependencies | No | All the implementations have been done in Matlab which offers efficient matrix operations. For SVD and NMF, we used the Matlab built-in functions and for the rest we have used our own efficient implementations (all the codes are available from the authors upon request). |
| Experiment Setup | Yes | For projected NMF and SVD, we found the best projection points by applying a grid search, with a step size of 0.05. For efficiency reasons, we mainly focus on p = 1 in the p-opt local search. As mentioned before, we refer to the method proposed in this paper as 1-opt-BMF, to the standard implementation as 1-opt-Standard and to the 1-opt local search associated with UBQP as 1-opt-UBQP. |