Partially Supervised Feature Selection with Regularized Linear Models
published: Aug. 26, 2009, recorded: June 2009, views: 3612
Report a problem or upload filesIf you have found a problem with this lecture or would like to send us extra material, articles, exercises, etc., please use our ticket system to describe your request and upload the data.
Enter your e-mail into the 'Cc' field, and we will keep you updated with your request's status.
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.
Link this pageWould you like to put a link to this lecture on your homepage?
Go ahead! Copy the HTML snippet !