Topic Graph Based Non-negative Matrix Factorization for Transfer Learning

We propose a method called Topic Graph based NMF for Transfer Learning (TNT) based on Non-negative Matrix Factorization (NMF). Since NMF learns feature vectors to approximate the given data, the proposed approach tries to preserve the feature space which is spanned by the feature vectors to realize transfer learning. Based on the learned feature vectors in the source domain, a graph structure called topic graph is constructed, and the graph is utilized as a regularization term in the framework of NMF. We show that the proposed regularization term corresponds to maximizing the similarity between topic graphs in both domains, and that the term corresponds to the graph Laplacian of the topic graph. Furthermore, we propose a learning algorithm with multiplicative update rules and prove its convergence. The proposed method is evaluated over document clustering problem, and the results indicate that the proposed method improves performance via transfer learning.