Rish I. et al. (ed.) Practical applications of sparse modeling

Boston: The MIT Press, 2014. — 252 p.Sparse modeling is a rapidly developing area at the intersection of statistical learning and signal processing, motivated by the age-old statistical problem of selecting a small number of predictive variables in high-dimensional data sets. This collection describes key approaches in sparse modeling, focusing on its applications in such fields as neuroscience, computational biology, and computer vision. Sparse modeling methods can improve the interpretability of predictive models and aid efficient recovery of high-dimensional unobserved signals from a limited number of measurements. Yet despite significant advances in the field, a number of open issues remain when sparse modeling meets real-life applications. The book discusses a range of practical applications and state-of-the-art approaches for tackling the challenges presented by these applications. Topics considered include the choice of method in genomics applications; analysis of protein mass-spectrometry data; the stability of sparse models in brain imaging applications; sequential testing approaches; algorithmic aspects of sparse recovery; and learning sparse latent modelsThe challenges of systems biology
Practical sparse modeling : an overview and two examples from genetics
High-dimensional sparse structured input-output models, with applications to GWAS
Sparse recovery for protein mass spectrometry data
Stability and reproducibility in fMRI analysis
Reliability estimation and enhancement via spatial smoothing in sparse fMRI modeling
Sequential testing for sparse recovery
Linear inverse problems with norm and sparsity constraints
Bayesian approaches for sparse latent variable models : reconsidering L₁ sparsity
Sparsity in topic models