Work

A Practical Algorithm for Topic Modeling with Provable Guarantees

Year: 2012

Type: preprint

Abstract: Topic models provide a useful method for dimensionality reduction and exploratory data analysis in large text corpora. Most approaches to topic model inference have been based on a maximum likelihood objective. Efficient algorithms exist that approximate this objective, but they have no provable guarantees. Recently, algorithms have been introduced that provide provable bounds, but these algorithms are not practical because they are inefficient and not robust to violations of model assumptions. In this paper we present an algorithm for topic model inference that is both provable and practical. The algorithm produces results comparable to the best MCMC implementations while running orders of magnitude faster.

Source: arXiv (Cornell University)

DOI: 10.48550/arxiv.1212.4777

Authors: Arora, Sanjeev, Ge, Rong, Halpern, Yoni, Mimno, David, Moitra, Ankur, Sontag, David, Wu, Yichen, Zhu, Michael

Institutions: Princeton University, New York University, Institute for Advanced Study

Language: English

Topic: Topic Modeling

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

Funders: National Science Foundation, Consortia for Improving Medicine with Innovation and Technology, York University, National Institutes of Health, Natural Sciences and Engineering Research Council of Canada, National Center for Advancing Translational Sciences

Awards: G2817352879, Collaborative Research: Understanding, Coping with, and Benefiting from Intractibility., AF: Small: Expansion, Unique Games, and Efficient Algorithms, CDI Type II: Pseudorandomness, G8023019573, G9000022544, G9000023129

OpenAlex ID: W2105617746

Cites: 19

Cited by: 331

Related to: 20