Multi-task co-clustering via nonnegative matrix factorization

Saining Xie, Hongtao Lu, Yangcheng He

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


Recent results have empirically proved that, given several related tasks with different data distributions and an algorithm that can utilize both the task-specific and cross-task knowledge, clustering performance of each task can be significantly enhanced. This kind of unsupervised learning method is called multi-task clustering. We focus on tackling the multi-task clustering problem via a 3-factor nonnegative matrix factorization. The object of our approach consists of two parts: (1) Within-task co-clustering: co-cluster the data in the input space individually. (2) Cross-task regularization: Learn and refine the relations of feature spaces among different tasks. We show that our approach has a sound information theoretic background and the experimental evaluation shows that it outperforms many state-of-the-art single-task or multi-task clustering methods.

Original languageEnglish (US)
Title of host publicationICPR 2012 - 21st International Conference on Pattern Recognition
Number of pages5
StatePublished - 2012
Event21st International Conference on Pattern Recognition, ICPR 2012 - Tsukuba, Japan
Duration: Nov 11 2012Nov 15 2012

Publication series

NameProceedings - International Conference on Pattern Recognition
ISSN (Print)1051-4651


Other21st International Conference on Pattern Recognition, ICPR 2012

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition


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