Unsupervised vocabulary discovery using non-negative matrix factorization with graph regularization

In this paper, we present a model for unsupervised pattern discovery using non-negative matrix factorization (NMF) with graph regularization. Though the regularization can be applied to many applications, we illustrate its effectiveness in a task of vocabulary acquisition in which a spoken utterance is represented by its histogram of the acoustic co-occurrences. The regularization expresses that temporally close co-occurrences should tend to end up in the same learned pattern. A novel algorithm that converges to a local optimum of the regularized cost function is proposed. Our experiments show that the graph regularized NMF model always performs better than the primary NMF model on the task of unsupervised acquisition of a small vocabulary.