A Double Parametric Bootstrap Test for Topic Models
Machine Learning
2017-11-22 v2
Abstract
Non-negative matrix factorization (NMF) is a technique for finding latent representations of data. The method has been applied to corpora to construct topic models. However, NMF has likelihood assumptions which are often violated by real document corpora. We present a double parametric bootstrap test for evaluating the fit of an NMF-based topic model based on the duality of the KL divergence and Poisson maximum likelihood estimation. The test correctly identifies whether a topic model based on an NMF approach yields reliable results in simulated and real data.
Keywords
Cite
@article{arxiv.1711.07104,
title = {A Double Parametric Bootstrap Test for Topic Models},
author = {Skyler Seto and Sarah Tan and Giles Hooker and Martin T. Wells},
journal= {arXiv preprint arXiv:1711.07104},
year = {2017}
}
Comments
Presented at NIPS 2017 Symposium on Interpretable Machine Learning