Transfer Spectral Clustering

Wenhao Jiang, Fu-Lai Chung

Abstract: Transferring knowledge from auxiliary datasets has been proved useful in machine learning tasks. Its adoption in clustering however is still limited. Despite of its superior performance, spectral clustering has not yet been incorporated with knowledge transfer or transfer learning. In this paper, we make such an attempt and propose a new algorithm called transfer spectral clustering (TSC). It involves not only the data manifold information of the clustering task but also the feature manifold information shared between related clustering tasks. Furthermore, it makes use of co-clustering to achieve and control the knowledge transfer among tasks. As demonstrated by the experimental results, TSC can greatly improve the clustering performance by effectively using auxiliary unlabeled data when compared with other state-of-the-art clustering algorithms.