Feature Mining through Distance Minimization Learning

  title={Feature Mining through Distance Minimization Learning},
  author={Jeffrey J. Thomson and Rex E. Gantenbein and Trevor Nielson},
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 2,1-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 2,1-norm regularization is that it encourages multiple predictors to share similar sparsity patterns… CONTINUE READING
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