Multi-task clustering via domain adaptation

• Published in: Pattern recognition Vol. 45; no. 1; pp. 465 - 473
• Main Authors: Zhang, Zhihao, Zhou, Jie
• Format: Journal Article
• Language: English
• Published: Kidlington Elsevier Ltd 2012; Elsevier
• Subjects: Adaptation; Applied sciences; Clustering; Convergence; Domain adaptation; Exact sciences and technology; Information, signal and communications theory; Multi-task clustering; Multi-task learning; Optimization; Pattern recognition; Signal and communications theory; Signal processing; Signal representation. Spectral analysis; Signal, noise; Subspaces; Tasks; Telecommunications and information theory; Multi-task clustering; Domain adaptation; Multi-task learning; Learning; Performance evaluation; Multiple task; Automatic classification; Subspace method; Pattern recognition; Signal classification; Optimization
• ISSN: 0031-3203; 1873-5142
• DOI: 10.1016/j.patcog.2011.05.011

Clustering is a fundamental topic in pattern recognition and machine learning research. Traditional clustering methods deal with a single clustering task on a single data set. However, in many real applications, multiple similar clustering tasks are involved simultaneously, e.g., clustering clients of different shopping websites, in which data of different subjects are collected for each task. These tasks are cross-domains but closely related. It is proved that we can improve the individual performance of each clustering task by appropriately utilizing the underling relation. In this paper, we will propose a new approach, which performs multiple related clustering tasks simultaneously through domain adaptation. A shared subspace will be learned through domain adaptation, where the gap of distributions among tasks is reduced, and the shared knowledge will be transferred through all tasks by exploiting the strengthened relation in the learned subspace. Then the object is set as the best clustering in both the original and learned spaces. An alternating optimization method is introduced and its convergence is theoretically guaranteed. Experiments on both synthetic and real data sets demonstrate the effectiveness of the proposed approach. ► We propose a novel multi-task clustering approach based on domain adaption. ► We can utilize strengthened relation in the shared space among multiple tasks. ► An alternating optimization method is introduced in this paper.