On a Guided Nonnegative Matrix Factorization
This addresses the issue of topic quality in document clustering for users of unsupervised models, but it is incremental as it builds on existing NMF methods.
The authors tackled the problem of unsupervised topic models learning meaningless or redundant topics due to biased data by proposing Guided NMF, which incorporates user-designed seed word supervision, and found it competitive with other methods with minimal supervision.
Fully unsupervised topic models have found fantastic success in document clustering and classification. However, these models often suffer from the tendency to learn less-than-meaningful or even redundant topics when the data is biased towards a set of features. For this reason, we propose an approach based upon the nonnegative matrix factorization (NMF) model, deemed \textit{Guided NMF}, that incorporates user-designed seed word supervision. Our experimental results demonstrate the promise of this model and illustrate that it is competitive with other methods of this ilk with only very little supervision information.