Co-Clustering for Multitask Learning

Keerthiram Murugesan; Jaime Carbonell; Yiming Yang

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arXiv · 1703.00994

Co-Clustering for Multitask Learning

Keerthiram Murugesan·Jaime Carbonell·Yiming Yang

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Abstract

This paper presents a new multitask learning framework that learns a shared representation among the tasks, incorporating both task and feature clusters. The jointly-induced clusters yield a shared latent subspace where task relationships are learned more effectively and more generally than in state-of-the-art multitask learning methods. The proposed general framework enables the derivation of more specific or restricted state-of-the-art multitask methods. The paper also proposes a highly-scalable multitask learning algorithm, based on the new framework, using conjugate gradient descent and generalized \textit{Sylvester equations}. Experimental results on synthetic and benchmark datasets show that the proposed method systematically outperforms several state-of-the-art multitask learning methods.

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BibTeX RIS

Keerthiram Murugesan, Jaime Carbonell, Yiming Yang. 2017-03-03. Co-Clustering for Multitask Learning. https://arxiv.org/abs/1703.00994

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