Learning Deep $\ell_0$ Encoders

Understand

Despite its nonconvex nature, $\ell_0$ sparse approximation is desirable in many theoretical and application cases.

• We study the $\ell_0$ sparse approximation problem with the tool of deep learning, by proposing Deep $\ell_0$ Encoders.
• Two typical forms, the $\ell_0$ regularized problem and the $M$-sparse problem, are investigated.
• Based on solid iterative algorithms, we model them as feed-forward neural networks, through introducing novel neurons and pooling functions.