Bayesian Nonparametric Space Partitions: A Survey

Fan, Xuhui; Li, Bin; Luo, Ling; Sisson, Scott A.

doi:10.24963/ijcai.2021/602

Bayesian Nonparametric Space Partitions: A Survey

Xuhui Fan, Bin Li, Ling Luo, Scott A. Sisson

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Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence. Survey Track. Pages 4408-4415.

https://doi.org/10.24963/ijcai.2021/602

Keywords

Machine learning: General

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.

Xuhui Fan, Bin Li, Ling Luo, and Scott A. Sisson. 2021. Bayesian Nonparametric Space Partitions: A Survey. In Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence, IJCAI-21. IJCAI, 4408–4415. https://doi.org/10.24963/ijcai.2021/602

@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    = {Prof. 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},
}

TY  - CPAPER
TI  - Bayesian Nonparametric Space Partitions: A Survey
AU  - Fan, Xuhui
AU  - Li, Bin
AU  - Luo, Ling
AU  - Sisson, Scott A.
T2  - Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence, IJCAI-21
PB  - International Joint Conferences on Artificial Intelligence Organization
PY  - 2021
SP  - 4408
EP  - 4415
DO  - 10.24963/ijcai.2021/602
UR  - https://doi.org/10.24963/ijcai.2021/602
ER  -