Multi-Task Learning in Heterogeneous Feature Spaces

Authors

  • Yu Zhang Hong Kong University of Science and Technology
  • Dit-Yan Yeung Hong Kong University of Science and Technology

Abstract

Multi-task learning aims at improving the generalization performance of a learning task with the help of some other related tasks. Although many multi-task learning methods have been proposed, they are all based on the assumption that all tasks share the same data representation. This assumption is too restrictive for general applications. In this paper, we propose a multi-task extension of linear discriminant analysis (LDA), called multi-task discriminant analysis (MTDA), which can deal with learning tasks with different data representations. For each task, MTDA learns a separate transformation which consists of two parts, one specific to the task and one common to all tasks. A by-product of MTDA is that it can alleviate the labeled data deficiency problem of LDA. Moreover, unlike many existing multi-task learning methods, MTDA can handle binary and multi-class problems for each task in a generic way. Experimental results on face recognition show that MTDA consistently outperforms related methods.

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Published

2011-08-04

How to Cite

Zhang, Y., & Yeung, D.-Y. (2011). Multi-Task Learning in Heterogeneous Feature Spaces. Proceedings of the AAAI Conference on Artificial Intelligence, 25(1), 574-579. Retrieved from https://ojs.aaai.org/index.php/AAAI/article/view/7909

Issue

Section

AAAI Technical Track: Machine Learning