Practical Applications of Sparse Modeling


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 datasets. This collection describes key approaches in sparse modeling, focusing on its applications in fields including 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 models.

A. Vania Apkarian, Marwan Baliki, Melissa K. Carroll, Guillermo A. Cecchi, Volkan Cevher, Xi Chen, Nathan W. Churchill, Rémi Emonet, Rahul Garg, Zoubin Ghahramani, Lars Kai Hansen, Matthias Hein, Katherine Heller, Sina Jafarpour, Seyoung Kim, Mladen Kolar, Anastasios Kyrillidis, Seunghak Lee, Aurelie Lozano, Matthew L. Malloy, Pablo Meyer, Shakir Mohamed, Alexandru Niculescu-Mizil, Robert D. Nowak, Jean-Marc Odobez, Peter M. Rasmussen, Irina Rish, Saharon Rosset, Martin Slawski, Stephen C. Strother, Jagannadan Varadarajan, Eric P. Xing

Table of Contents

  1. Series Foreword
  2. 1. Introduction

    Irina Rish, Guillermo A. Cecchi, Aurelie Lozano, and Alexandru Niculescu-Mizil

  3. 2. The Challenges of Systems Biology

    Pablo Meyer and Guillermo A. Cecchi

  4. 3. Practical Sparse Modeling An Overview and Two Examples from Genetics

    Saharon Rosset

  5. 4. High-Dimensional Sparse Structured Input-Output Models, with Applications to GWAS

    Eric P. Xing, Mladen Kolar, Seyoung Kim, and Xi Chen

  6. 5. Sparse Recovery for Protein Mass Spectrometry Data

    Martin Slawski and Matthias Hein

  7. 6. Stability and Reproducibility in fMRI Analysis

    Stephen C. Strother, Peter M. Rasmussen, Nathan W. Churchill, and Lars Kai Hansen

  8. 7. Reliability Estimation and Enhancement via Spatial Smoothing in Sparse fMRI Modeling

    Melissa K. Carroll, Guillermo A. Cecchi, Irina Rish, Rahul Garg, Marwan Baliki, and A. Vania Apkarian

  9. 8. Sequential Testing for Sparse Recovery

    Matthew L. Malloy and Robert D. Nowak

  10. 9. Linear Inverse Problems with Norm and Sparsity Constraints

    Volkan Cevher, Sina Jafarpour, and Anastasios Kyrillidis

  11. 10. Bayesian Approaches for Sparse Latent Variable Models: Reconsidering l1 Sparsity

    Shakir Mohamed, Katherine Heller, and Zoubin Ghahramani

  12. 11. Sparsity in Topic Models

    Jagannadan Varadarajan, Rémi Emonet, and Jean-Marc Odobez

  13. References
  14. Index