  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.
