Author
Kovacs, R
Gunluk, O
Hauser, R
Journal title
Proceedings of the 35th AAAI Conference on Artificial Intelligence (AAAI-21)
Issue
5
Volume
35
Last updated
2024-02-17T15:38:15.96+00:00
Page
3823-3831
Abstract
Identifying discrete patterns in binary data is an important dimensionality reduction tool in machine learning and data mining. In this paper, we consider the problem of low-rank binary matrix factorisation (BMF) under Boolean arithmetic. Due to the hardness of this problem, most previous attempts rely on heuristic techniques. We formulate the problem as a mixed integer linear program and use a large scale optimisation technique of column generation to solve it without the need of heuristic pattern mining. Our approach focuses on accuracy and on the provision of optimality guarantees. Experimental results on real world datasets demonstrate that our proposed method is effective at producing highly accurate factorisations and improves on the previously available best known results for 15 out of 24 problem instances.
Symplectic ID
1165789
Favourite
Off
Publication type
Conference Paper
ISBN-13
978-1-57735-866-4
Publication date
18 May 2021
Please contact us with feedback and comments about this page. Created on 03 Mar 2021 - 14:44.