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Stability of Topic Modeling via Matrix Factorization

Authors: 

Mark Belford, Brian Mac Namee, Derek Greene

Publication Type: 
Refereed Original Article
Abstract: 
Topic models can provide us with an insight into the underlying latent structure of a large corpus of documents. A range of methods have been proposed in the literature, including probabilistic topic models and techniques based on matrix factorization. However, in both cases, standard implementations rely on stochastic elements in their initialization phase, which can potentially lead to different results being generated on the same corpus when using the same parameter values. This corresponds to the concept of “instability” which has previously been studied in the context of k-means clustering. In many applications of topic modeling, this problem of instability is not considered and topic models are treated as being definitive, even though the results may change considerably if the initialization process is altered. In this paper we demonstrate the inherent instability of popular topic modeling approaches, using a number of new measures to assess stability. To address this issue in the context of matrix factorization for topic modeling, we propose the use of ensemble learning strategies. Based on experiments performed on annotated text corpora, we show that a K-Fold ensemble strategy, combining both ensembles and structured initialization, can significantly reduce instability, while simultaneously yielding more accurate topic models
Digital Object Identifer (DOI): 
10.1016/j.eswa.2017.08.047
Publication Status: 
Published
Date Accepted for Publication: 
Monday, 28 August, 2017
Publication Date: 
21/09/2016
Journal: 
Expert Systems with Applications Volume 91, January 2018, Pages 159-169
Institution: 
National University of Ireland, Dublin (UCD)
Open access repository: 
No