A multi-task learning strategy for unsupervised clustering via explicitly separating the commonality

In this paper, we propose an unsupervised cluster method via a multi-task learning strategy, called Mt-Cluster. Our MtCluster learns a cluster-specific dictionary for each cluster to represent its sample signals and a shared common pattern pool (the commonality) for the essentially complemental representation. By treating learning the cluster-specific dictionary as a single task, MtCluster works in a multi-task learning manner, in which all the tasks are connected by simultaneously learning the commonality. Actually, the learned cluster-specific dictionary spans the feature space of the corresponding cluster, and the commonality is just used for necessary complemental representation. To evaluate our method, we perform several experiments on public available datasets, and the promising results demonstrate the effectiveness of MtCluster.