NeurIPS 2017poster20 citations
Conic Scan-and-Cover algorithms for nonparametric topic modeling
Mikhail Yurochkin, Aritra Guha, Xuanlong Nguyen
Abstract
We propose new algorithms for topic modeling when the number of topics is unknown. Our approach relies on an analysis of the concentration of mass and angular geometry of the topic simplex, a convex polytope constructed by taking the convex hull of vertices representing the latent topics. Our algorithms are shown in practice to have accuracy comparable to a Gibbs sampler in terms of topic estimation, which requires the number of topics be given. Moreover, they are one of the fastest among several state of the art parametric techniques. Statistical consistency of our estimator is established under some conditions.
BibTeX
@inproceedings{NIPS2017_9185f3ec,
author = {Yurochkin, Mikhail and Guha, Aritra and Nguyen, XuanLong},
booktitle = {Advances in Neural Information Processing Systems},
editor = {I. Guyon and U. Von Luxburg and S. Bengio and H. Wallach and R. Fergus and S. Vishwanathan and R. Garnett},
pages = {},
publisher = {Curran Associates, Inc.},
title = {Conic Scan-and-Cover algorithms for nonparametric topic modeling},
url = {https://proceedings.neurips.cc/paper_files/paper/2017/file/9185f3ec501c674c7c788464a36e7fb3-Paper.pdf},
volume = {30},
year = {2017}
}