Random tessellation forests

Conference item

Space partitioning methods such as random forests and the Mondrian process are powerful machine learning methods for multi-dimensional and relational data, and are based on recursively cutting a domain. The flexibility of these methods is often limited by the requirement that the cuts be axis aligned. The Ostomachion process and the self-consistent binary space partitioning-tree process were recently introduced as generalizations of the Mondrian process for space partitioning with non-axis aligned cuts in the plane. Motivated by the need for a multi-dimensional partitioning tree with non-axis aligned cuts, we propose the Random Tessellation Process, a framework that includes the Mondrian process as a special case. We derive a sequential Monte Carlo algorithm for inference, and provide random forest methods. Our methods are self-consistent and can relax axis-aligned constraints, allowing complex inter-dimensional dependence to be captured. We present a simulation study and analyze gene expression data of brain tissue, showing improved accuracies over other methods.

Authors: Ge, S; Wang, S; Teh, YW; Wang, L; Elliott, LT

• Publication status: Published
• Peer review status: Peer reviewed
• Publisher: Curran Associates
• Host title: Advances in Neural Information Processing Systems 32: 33rd Conference on Neural Information Processing Systems (NeurIPS 2019)
• Volume: 12
• Pages: 9543-9553
• Series: Advances in Neural Information Processing Systems
• Publication date: 2019-12-10
• Acceptance date: 2019-09-04
• Event title: Advances in Neural Information Processing Systems 32
• Event location: Vancouver, Canada
• Event start date: 2019-12-08
• Event end date: 2019-12-14
• ISSN: 1049-5258
• ISBN: 978-1-7138-0793-3
• Language: English