Conic Scan-and-Cover algorithms for nonparametric topic modeling

2017/10/09 by Mikhail Yurochkin, Yurochkin, Mikhail, Aritra Guha +3
Computer Science · #Bayesian Methods and Mixture Models #Natural Language Processing Techniques #Topic Modeling

paper · pdf · doi:10.48550/arxiv.1710.02952

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.

Citations

• Least squares quantization in PCM

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