特征尺寸次线性增长的不可交换特征分配模型
Non-exchangeable feature allocation models with sublinear growth of the feature sizes
摘要：
特征分配模型是在不同应用中使用的流行模型，例如无监督学习或网络建模。特别是，印度自助餐过程是一个灵活而简单的单参数特征分配模型，其中特征的数量随着对象的数量无限增长。像大多数特征分配模型一样，印度自助餐过程满足交换的对称性：对象的排列下分布不变。虽然这种特性在某些情况下是可取的，但它有一些强烈的含义。重要的是，共享特定特征的对象的数量随着对象的数量线性增长。在这篇文章中，我们描述了一类不可交换的功能分配模型，其中共享给定功能的对象的数量呈次线性增长，其中速率可以通过调整参数来控制。我们推导了模型的渐近性质，并表明这种模型在各种数据集上提供了更好的拟合和更好的预测性能。
英文摘要：
Feature allocation models are popular models used in different applications such as unsupervised learning or network modeling. In particular, the Indian buffet process is a flexible and simple one-parameter feature allocation model where the number of features grows unboundedly with the number of objects. The Indian buffet process, like most feature allocation models, satisfies a symmetry property of exchangeability: the distribution is invariant under permutation of the objects. While this property is desirable in some cases, it has some strong implications. Importantly, the number of objects sharing a particular feature grows linearly with the number of objects. In this article, we describe a class of non-exchangeable feature allocation models where the number of objects sharing a given feature grows sublinearly, where the rate can be controlled by a tuning parameter. We derive the asymptotic properties of the model, and show that such model provides a better fit and better predictive performances on various datasets.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
