Learning a Kernel for Multi-Task Clustering

Authors

  • Quanquan Gu University of Illinois at Urbana-Champaign
  • Zhenhui Li University of Illinois at Urbana-Champaign
  • Jiawei Han University of Illinois at Urbana-Champaign

DOI:

https://doi.org/10.1609/aaai.v25i1.7914

Abstract

Multi-task learning has received increasing attention in the past decade. Many supervised multi-task learning methods have been proposed, while unsupervised multi-task 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.

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Published

2011-08-04

How to Cite

Gu, Q., Li, Z., & Han, J. (2011). Learning a Kernel for Multi-Task Clustering. Proceedings of the AAAI Conference on Artificial Intelligence, 25(1), 368-373. https://doi.org/10.1609/aaai.v25i1.7914

Issue

Section

AAAI Technical Track: Machine Learning