3 accepted papers
Unseen data, which are not samples from the distribution of training data and are difficult to collect, have exhibited importance in numerous applications, ({\em e.g.,} novelty detection, semi-supervised learning, and adversarial training). In this paper, we introduce a general framework called \t…
We address the problem of compressed sensing with multiple measurement vectors associated with prior information in order to better reconstruct an original sparse signal. This problem is modeled via convex optimization with ℓ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://ww…
In sparse signal recovery of compressive sensing, the phase transition determines the edge, which separates successful recovery and failed recovery. Moreover, the width of phase transition determines the vague region, where sparse recovery is achieved in a probabilistic manner. Earlier works on phas…