Random subspaces NMF for unsupervised transfer learning

In this paper we propose a new unsupervised transfer learning approach which aims at finding a partition of unlabeled data in target domain using the knowledge obtained from clustering a source domain unlabeled data. The key idea behind our method is that finding partitions in different feature's subspaces of a source task can help to obtain a more accurate partition in a target one. From the set of source partitions we select only k nearest neighbors using some measure of similarity. Finally, multi-layer non-negative matrix factorization is performed to obtain a partition of objects in target domain. Experimental results show high potential and effectiveness of the proposed technique.