Material Detail

Partially Supervised Feature Selection with Regularized Linear Models

Partially Supervised Feature Selection with Regularized Linear Models

This video was recorded at 26th International Conference on Machine Learning (ICML), Montreal 2009. This paper addresses feature selection techniques for classification of high dimensional data, such as those produced by microarray experiments. Some prior knowledge may be available in this context to bias the selection towards some dimensions (genes) a priori assumed to be more relevant. We propose a feature selection method making use of this partial supervision. It extends previous works on embedded feature selection with linear models including regularization to enforce sparsity. A practical approximation of this technique reduces to standard SVM learning with iterative rescaling of the inputs. The scaling factors depend here on the prior knowledge but the final selection may depart from it. Practical results on several microarray data sets show the benefits of the proposed approach in terms of the stability of the selected gene lists with improved classification performances.


  • User Rating
  • Comments
  • Learning Exercises
  • Bookmark Collections
  • Course ePortfolios
  • Accessibility Info

More about this material


Log in to participate in the discussions or sign up if you are not already a MERLOT member.