Subspace pursuit for gene profile classificaiton

  • Hang X
  • Dai W
  • Wu F
  • 2


    Mendeley users who have this article in their library.
  • 2


    Citations of this article.


Gene profile classification is achieved by casting the classification problem as finding the sparse representation of testing samples with respect to training samples. The sparse representation is found by subspace pursuit, which is much more efficient than linear programming techniques. The new approach, with no need of model selection, however, still has the performance which can match the best result achieved among all the SVM variants after careful model selection.

Get free article suggestions today

Mendeley saves you time finding and organizing research

Sign up here
Already have an account ?Sign in

Find this document


  • Xiyi Hang

  • Wei Dai

  • Fang Xiang Wu

Cite this document

Choose a citation style from the tabs below

Save time finding and organizing research with Mendeley

Sign up for free