Delegation-Relegation for Boolean Matrix Factorization
Authors: Florent Avellaneda, Roger Villemaire
AAAI 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Furthermore, our experiments show that our approach outperforms existing exact BMF algorithms. In this paper, we present a new approach to simplifying the matrix to be factorized by reducing the number of 1-entries, which allows to directly recover a Boolean factorization of the original matrix from its simplified version. We conducted an evaluation of our methods, Simpli and Simpli , focusing on two key aspects: the degree of simplification they achieve and their effect on the time savings when performing factorizations on the simplified matrices using existing constraint-based BMF solvers. |
| Researcher Affiliation | Academia | Florent Avellaneda1, 2, Roger Villemaire1 1 Universit e du Qu ebec a Montr eal (UQAM), Montr eal, Canada 2 Centre de Recherche de l Institut Universitaire de G eriatrie de Montr eal, Montr eal, Canada avellaneda.florent@uqam.ca, villemaire.roger@uqam.ca |
| Pseudocode | Yes | Algorithm 1: Simpli OP; Algorithm 2: BMF through simplified matrix |
| Open Source Code | Yes | 1https://github.com/Florent Avellaneda/Delegation BMF |
| Open Datasets | Yes | We considered 31 classic datasets from the literature, namely: Audiology , Autism , Balance Scale , Brest Cancer , Car Evaluation , Chess , Contraceptive Method Choice , Dermatology , Firewall , Solar Flare , Heart Disease , Hepatitis , Iris , Lymphography , Mushroom , Nursery , Website Phishing , Soybean , Student Performance , Thoracic Surgery , Tic Tac-Toe Endgame , Primary Tumor , Voting Records , Wine , Zoo from UCI (Kelly, Longjohn, and Nottingham 2023), Americas-small , Apj , Customer from (Ene et al. 2008), DNA from (Myllykangas et al. 2006) and Paleo from (ˇZliobait e et al. 2023). |
| Dataset Splits | No | The paper describes experiments on Boolean Matrix Factorization, which operates on single matrices rather than using traditional machine learning train/validation/test splits. Therefore, such dataset splits are not explicitly provided for reproducing the experiment in this context. |
| Hardware Specification | Yes | Our algorithms were implemented in C++1, and we performed the experiments on an Intel Gold 6148 Skylake processor using a single thread and 32 Go of RAM. |
| Software Dependencies | No | The paper states that algorithms were implemented in C++ and mentions the use of existing BMF solvers (CG and Opti Block), but it does not specify version numbers for C++ compilers or any specific libraries used within the implementation, nor for the solvers themselves beyond their names. |
| Experiment Setup | Yes | We set a time limit of 3 hours and recorded the execution time and rank found for CG in Table 1 and for Opti Block in Table 2. |