ABSTRACT. Large-scale topic models serve as basic tools for feature extraction and dimensionality reduction in many practical applications.

As a natural extension of flat topic models, hierarchical topic models (HTMs) are able to learn topics of different levels of abstraction, which lead to deeper ...

ABSTRACT. Large-scale topic models serve as basic tools for feature extraction and dimensionality reduction in many practical applications.

Nov 21, 2024 · Large-scale topic models serve as basic tools for feature extraction and dimensionality reduction in many practical applications.

This paper proposes an efficient partially collapsed Gibbs sampling algorithm for hLDA, as well as an initialization strategy to deal with local optima ...

Jun 6, 2019 · In this work, we study data-parallel training for the hierarchical Dirichlet process (HDP) topic model. Based upon a representation of certain ...

May 20, 2024 · Abstract. Hierarchical topic modeling, which can mine implicit semantics in the corpus and automatically construct topic.

We propose a new framework called as Contrastive Learning for Hierarchical Topic Modeling (CLHTM), which can efficiently mine hierarchical topics based on ...

Our approach provides novel and scalable algorithms for learning nonparametric topic models of text docu- ments and Gaussian admixture models of im- age patches ...

Hierarchical topic modeling extends topic modeling by extracting topics and organizing them into a hierarchical structure. In this study, we combine the two and ...