3 accepted papers
The determinantal point process (DPP) is an elegant probabilistic model of repulsion with applications in various machine learning tasks including summarization and search. However, the maximum a posteriori (MAP) inference for DPP which plays an important role in many applications is NP-hard, and ev…
Sparse signal recovery in the static case has been well studied under the framework of Compressive Sensing (CS), while in recent years more attention has also been paid to the dynamic case. In this paper, enlightened by the idea of modified-CS with partially known support, and based on a non-convex…
In solving the problem of sparse recovery, non-convex techniques have been paid much more attention than ever before, among which the most widely used one is ℓ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">p</sub> minimization with p ∈ (0, 1). It has…