Multi-task feature learning via efficient l 2, 1 -norm minimization
UAI 2009
Pages: 339 - 348
Published: Jun 18, 2009
Abstract
The problem of joint feature selection across a group of related tasks has applications in many areas including biomedical informatics and computer vision. We consider the l2, 1-norm regularized regression model for joint feature selection from multiple tasks, which can be derived in the probabilistic framework by assuming a suitable prior from the exponential family. One appealing feature of the l2, 1-norm regularization is that it encourages...
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