Proximal Methods for Hierarchical Sparse Coding

Understand

Sparse coding consists in representing signals as sparse linear combinations of atoms selected from a dictionary.

• We consider an extension of this framework where the atoms are further assumed to be embedded in a tree.
• This is achieved using a recently introduced tree-structured sparse regularization norm, which has proven useful in several applications.
• This norm leads to regularized problems that are difficult to optimize, and we propose in this paper efficient algorithms for solving them.