Over the past decade, sparsity has emerged as a dominant theme in signal processing and big data applications. In this chapter, we formulate and solve new flavors of sparsity-constrained optimization problems built on the family of spike-and-slab priors. First, we develop an efficient Iterative Convex Refinement solution to the hard non-convex problem of Bayesian signal recovery under sparsity-inducing spike-and-slab priors. We also offer a Bayesian perspective on sparse representation-based classification via the introduction of class-specific priors. This formulation represents a consummation of ideas developed for model-based compressive sensing into a general framework for sparse model-based classification.
|Original language||English (US)|
|Title of host publication||Handbook of Convex Optimization Methods in Imaging Science|
|Publisher||Springer International Publishing|
|Number of pages||30|
|State||Published - Jan 1 2017|
All Science Journal Classification (ASJC) codes
- Computer Science(all)