Learning a Kernel for Multi-Task Clustering

Quanquan Gu, Zhenhui Li, Jiawei Han

Research output: Chapter in Book/Report/Conference proceedingConference contribution


Multi-task learning has received increasing attention in the past decade. Many supervised multi-task learning methods have been proposed, while unsupervised multitask learning is still a rarely studied problem. In this paper, we propose to learn a kernel for multi-task clustering. Our goal is to learn a Reproducing Kernel Hilbert Space, in which the geometric structure of the data in each task is preserved, while the data distributions of any two tasks are as close as possible. This is formulated as a unified kernel learning framework, under which we study two types of kernel learning: nonparametric kernel learning and spectral kernel design. Both types of kernel learning can be solved by linear programming. Experiments on several cross-domain text data sets demonstrate that kernel k-means on the learned kernel can achieve better clustering results than traditional single-task clustering methods. It also outperforms the newly proposed multi-task clustering method.

Original languageEnglish (US)
Title of host publicationProceedings of the 25th AAAI Conference on Artificial Intelligence, AAAI 2011
PublisherAmerican Association for Artificial Intelligence (AAAI) Press
Number of pages6
ISBN (Electronic)9781577355083
StatePublished - Aug 11 2011
Event25th AAAI Conference on Artificial Intelligence, AAAI 2011 - San Francisco, United States
Duration: Aug 7 2011Aug 11 2011

Publication series

NameProceedings of the 25th AAAI Conference on Artificial Intelligence, AAAI 2011


Conference25th AAAI Conference on Artificial Intelligence, AAAI 2011
Country/TerritoryUnited States
CitySan Francisco

ASJC Scopus subject areas

  • Artificial Intelligence


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