A Framework for Learning Predicative Structures from Multiple Tasks and Unlabeled Data

by Rie Kubota Ando and Tong Zhang

This paper addresses a framework for multi-task learning. Their idea is quite simple. There is a common factor \Theta which is shared in different but related problems. Therefore in each problem P_k, our parameters include w_i, which is problem-specific and v_i which is dependent on the common feature controled by \Theta. To solve the model it usually desirable to alternatively optimize over w_i, v_i and \Theta. Usually a regularizer is also included for better generalization capacity.

using this idea, the authors proposed a linear model which is solved by the ASO using SVD in each iteration to find \Theta (SVD-ASO in their term). With this idea, they analyzed the semi-supervised learning with auxiliary functions, which are essentially those multi-tasks.

Their extention for this piece of work is scanned here.