We propose sparsity and grouped sparsity inducing priors on the meta parameters of word topic probabilities in fully Bayesian Latent Dirichlet Alloca- tion (LDA) ...

Topic Models with Sparse and Group-Sparsity Inducing Priors. Download PDF · Christian Pölitz. Published: 31 Dec 2015, Last Modified: 17 Jun 2024DMNLP@PKDD/ECML ...

Bibliographic details on Topic Models with Sparse and Group-Sparsity Inducing Priors.

Title: Topic Models with Sparse and Group-Sparsity Inducing Priors ; Authors: Christian Pölitz ; Authorids: Christian Pölitz ; Venue: DMNLP@PKDD/ECML 2016 ; Venueid ...

Oct 9, 2016 · Sparsity-inducing generally means that each document will have a small number of topics, and/or each topic a small number of words.

Jun 15, 2018 · In this paper, we propose a novel Bayesian hierarchical topic models called Bayesian Sparse Topical Coding with Poisson Distribution (BSTC-P)

Feb 15, 2014 · The sparsity that SAGE refers to is in these topic distributions -- SAGE has sparsity in topic distributions over words, but LDA does not.

Dec 18, 2015 · ABSTRACT. Learning low dimensional representations of text corpora is critical in many content analysis and data mining applications.

In this paper, we propose a dual-sparse topic model that addresses the sparsity in both the topic mixtures and the word usage. By applying a "Spike and Slab" ...

GS-LDA is a novel approach to inducing interpretability by exploiting structured vocabularies. Prior work on inter- pretable topic models has focused on various ...