Bayesian Nonparametric Space Partitions: A Survey
Xuhui Fan, Bin Li, Ling Luo, Scott A. Sisson
Abstract
Bayesian nonparametric space partition (BNSP) models provide a variety of strategies for partitioning a D-dimensional space into a set of blocks, such that the data within the same block share certain kinds of homogeneity. BNSP models are applicable to many areas, including regression/classification trees, random feature construction, and relational modelling. This survey provides the first comprehensive review of this subject. We explore the current progress of BNSP research through three perspectives: (1) Partition strategies, where we review the various techniques for generating partitions and discuss their theoretical foundation, `self-consistency'; (2) Applications, where we detail the current mainstream usages of BNSP models and identify some potential future applications; and (3) Challenges, where we discuss current unsolved problems and possible avenues for future research.
BibTeX
@inproceedings{ijcai2021p602,
title = {Bayesian Nonparametric Space Partitions: A Survey},
author = {Fan, Xuhui and Li, Bin and Luo, Ling and Sisson, Scott A.},
booktitle = {Proceedings of the Thirtieth International Joint Conference on
Artificial Intelligence, {IJCAI-21}},
publisher = {International Joint Conferences on Artificial Intelligence Organization},
editor = {Zhi-Hua Zhou},
pages = {4408--4415},
year = {2021},
month = {8},
note = {Survey Track},
doi = {10.24963/ijcai.2021/602},
url = {https://doi.org/10.24963/ijcai.2021/602},
}