GLSVM: Integrating Structured Feature Selection and Large Margin Classification

Date Added: Sep 2009
Format: PDF

High dimensional data challenges current feature selection methods. For many real world problems the authors often have prior knowledge about the relationship of features. For example in microarray data analysis, genes from the same biological pathways are expected to have similar relationship to the outcome that they target to predict. Recent regularization methods on Support Vector Machine (SVM) have achieved great success to perform feature selection and model selection simultaneously for high dimensional data, but neglect such relationship among features. To build interpretable SVM models, the structure information of features should be incorporated.