Binary matrix factorisation via column generation

Conference item

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.

Authors: Kovacs, RA; Gunluk, O; Hauser, R

• Publication status: Published
• Peer review status: Peer reviewed
• Publisher: Association for the Advancement of Artificial Intelligence
• Host title: Proceedings of the 35th AAAI Conference on Artificial Intelligence (AAAI-21)
• Volume: 35
• Issue: 5
• Pages: 3823-3831
• Publication date: 2021-05-18
• Acceptance date: 2020-12-02
• Event title: 35th AAAI Conference on Artificial Intelligence (AAAI-21)
• Event location: Virtual
• Event website: https://aaai.org/Conferences/AAAI-21/
• Event start date: 2021-02-02
• Event end date: 2021-02-09
• EISSN: 2374-3468
• ISSN: 2159-5399
• ISBN: 978-1-57735-866-4
• Language: English
• Keywords: FFR