Concentrated Document Topic Model
Machine Learning
2021-02-10 v1 Information Retrieval
Machine Learning
Abstract
We propose a Concentrated Document Topic Model(CDTM) for unsupervised text classification, which is able to produce a concentrated and sparse document topic distribution. In particular, an exponential entropy penalty is imposed on the document topic distribution. Documents that have diverse topic distributions are penalized more, while those having concentrated topics are penalized less. We apply the model to the benchmark NIPS dataset and observe more coherent topics and more concentrated and sparse document-topic distributions than Latent Dirichlet Allocation(LDA).
Cite
@article{arxiv.2102.04449,
title = {Concentrated Document Topic Model},
author = {Hao Lei and Ying Chen},
journal= {arXiv preprint arXiv:2102.04449},
year = {2021}
}
Comments
arXiv admin note: text overlap with arXiv:2102.03525