Multi-task clustering through instances transfer

We propose a multi-task clustering method by transferring knowledge of instances.The sample distance in different tasks is reweighted by learning a shared subspace.Related samples from other tasks are reused as auxiliary data to aid clustering.Our method maintains the label marginal distribution of each individual task.Better performance is observed compared with other multi-task clustering methods. Clustering is an essential issue in machine learning and data mining. As there are many related tasks in the real world, multi-task clustering, which improves the clustering performance of each task by transferring knowledge across the related tasks, receives increasing attention recently. Generally knowledge transfer can be accomplished in different ways. Nevertheless, besides transferring knowledge of feature representations, other knowledge transfer ways have seldom been adopted for multi-task clustering. In this paper, we propose a general multi-task clustering algorithm by transferring knowledge of instances. Our algorithm reweights the distance between samples in different tasks by learning a shared subspace, then selects the nearest neighbors for each sample from the other tasks in the learned shared subspace as the auxiliary data to aid the clustering process of each individual task. Experiments on real data sets in text mining and image mining demonstrate that our proposed algorithm outperforms the traditional single-task clustering methods and existing cross-domain multi-task clustering methods.