Sparse information representation through feature selection

In this talk I am presenting a range of feature selection methods, which are aimed at detecting the most parsimonious subset of characteristics/features/genes. This sparse representation leads always to simpler, more interpretable models, and may lead to improvement in prediction accuracy. I survey some of the state-of-the-art developed algorithms, and discuss a novel approach which is both computationally attractive, and seems to work very effectively across a range of domains, in particular for fat datasets.