论文标题
贝叶斯非参数空间分区：一项调查
Bayesian Nonparametric Space Partitions: A Survey
论文作者
论文摘要
贝叶斯非参数太空分区（BNSP）模型提供了各种策略，将$ d $维空间划分为一组块。这样，数据点位于同一块中，将共享某些类型的同质性。 BNSP模型可以应用于各个领域，例如回归/分类树，随机特征构造，关系建模等。在本调查中，我们通过以下三个观点研究了BNSP研究的当前进展：模型，这些模型审查了在空间中产生分区并讨论其理论基础的“自我关系”的各种策略；涵盖BNSP模型当前主流用法及其潜在的未来实践的应用程序；和挑战，这些挑战确定了当前未解决的问题和有价值的未来研究主题。由于以前没有关于BNSP文献的全面评论，因此我们希望这项调查能够引起对该主题的进一步探索和剥削。
Bayesian nonparametric space partition (BNSP) models provide a variety of strategies for partitioning a $D$-dimensional space into a set of blocks. In this way, the data points lie in the same block would share certain kinds of homogeneity. BNSP models can be applied to various areas, such as regression/classification trees, random feature construction, relational modeling, etc. In this survey, we investigate the current progress of BNSP research through the following three perspectives: models, which review various strategies for generating the partitions in the space and discuss their theoretical foundation `self-consistency'; applications, which cover the current mainstream usages of BNSP models and their potential future practises; and challenges, which identify the current unsolved problems and valuable future research topics. As there are no comprehensive reviews of BNSP literature before, we hope that this survey can induce further exploration and exploitation on this topic.
