Scalable Probabilistic Frames
arXiv:1501.073212 citations
Originality Synthesis-oriented
AI Analysis
For researchers in frame theory, this work extends scalability results to probabilistic frames, but is incremental as it adapts known concepts.
The paper investigates scalability of probabilistic frames, providing conditions under which a discrete probabilistic frame can be rescaled to a tight frame.
We consider the problem of rescaling the lengths of a finite frame thereby transforming it into a tight one. Such frames are called scalable and have received a lot of attention in recent years. In this note we investigate the question in terms of probabilistic frames and give conditions under which a (discrete) probabilistic frame is scalable.