2019年7月5日 · 而分层Dirichlet过程（Hierarchical Dirichlet Process, HDP）则是无参数非参数模型，它扩展了LDA，可以处理无限的主题层级。"hdp-lda"项目是将这两种概念结合的C++实 …

Hierarchical Latent Dirichlet Allocation (hLDA) addresses the problem of learning topic hierarchies from data. The model relies on a non-parametric prior called the nested Chinese restaurant …

This code implements hierarchical LDA with a fixed depth tree and a stick breaking prior on the depth weights. An infinite-depth tree can be approximated by setting the depth to be very high. …

2014年10月23日 · hLDA模型将此先验与基于潜在Dirichlet分配的分层变体的可能性相结合。 执行 是用于hLDA推断的Gibbs采样器，基于的实现，在nCRP树上具有固定的深度。 安装 只需使 …

In this paper, we propose a novel method called hierarchical LDA (h-LDA) that formulates a new feature extraction method based on hierarchical cluster structure of depth two in the data. In h …

2024年6月14日 · hLDA是一种先进的统计模型，用于学习数据中的主题层次结构。 不同于传统的Latent Dirichlet Allocation（LDA），它引入了非参数性的嵌套Chinese Restaurant …

hLDA是一种无监督贝叶斯非参方法，不仅能在大规模离散无结构数据中挖掘潜在主题，组织成更符合人类认知的层次语义结构，而且能自动适应开放数据集的增长。 相对已有的英文hLDA摘 …

2019年11月15日 · Here we can see the difference from LDA’s use of Dir Distribution: DP directly samples to generate a probability measure, which can further generate a discrete probability …

Abstract: Inspired by the recent success of hierarchical representation, we propose a new hierarchical variant of latent Dirichlet allocation (h-LDA) for action recognition. The model …

Dear All, please note that the HDP and h-LDA are two distinct mathematical modelling approaches. h-LDA will allocate vocabulary to topics such that the topics are arranged in a …