Transfer heterogeneous unlabeled data for unsupervised clustering

In this paper, we propose a novel method called THUNTER to transfer the heterogenous unlabeled data from the source domain to the target domain for clustering. Suppose the target data are a set of images, then the so-called heterogeneous unlabeled data can be a large set of text data or acoustic data. Our method aims to address how to transfer these large amount of heterogeneous data to the relatively smaller target data set for clustering. To the best of our knowledge, it is the first work in the community to transfer the unlabeled data, especially the unlabeled heterogeneous data, for unsupervised clustering. Furthermore, along with our method, a novel dictionary-based data transfer strategy (DicTrans) is introduced in this paper, which measures the fidelity of transferring the target data to the source domain and automatically decides how many to transfer. Through a series of experiments, the effectiveness of THUNTER and DicTrans are demonstrated with very promising performances.