论文标题
收敛的印度自助餐过程
The convergent Indian buffet process
论文作者
论文摘要
我们提出了潜在特征模型的新贝叶斯非参数先验，我们称之为收敛的印度自助餐过程（CIBP）。我们表明，在CIBP下，潜在特征的数量分布为泊松分布，平均单调增加，但随着对象的数量流向无穷大。也就是说，即使对象的数量变为无穷大，预期的功能数量也是上面的，与标准的印度自助过程不同，预期的特征数量随物体数量增加。我们根据分层分布和完全随机的度量提供了两种CIBP的替代表示，这些表示具有独立感兴趣。提出的CIBP在高维稀疏因子模型上进行评估。
We propose a new Bayesian nonparametric prior for latent feature models, which we call the convergent Indian buffet process (CIBP). We show that under the CIBP, the number of latent features is distributed as a Poisson distribution with the mean monotonically increasing but converging to a certain value as the number of objects goes to infinity. That is, the expected number of features is bounded above even when the number of objects goes to infinity, unlike the standard Indian buffet process under which the expected number of features increases with the number of objects. We provide two alternative representations of the CIBP based on a hierarchical distribution and a completely random measure, respectively, which are of independent interest. The proposed CIBP is assessed on a high-dimensional sparse factor model.
