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论文类型：期刊论文

发表时间：2017-08-16

发表刊物：NEUROCOMPUTING

收录刊物：SCIE、EI、Scopus

卷号：251

页面范围：145-155

ISSN号：0925-2312

关键字：Multi-task clustering; Instances transfer; Shated nearest neighbor similarity

摘要：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. (C) 2017 Elsevier B.V. All rights reserved.