Revisiting RIP guarantees for sketching operators on mixture models
Ayoub Belhadji, R\'emi Gribonval

TL;DR
This paper reexamines RIP guarantees for sketching operators in mixture models, proposing a new analysis that removes importance sampling assumptions and enables structured sketching guarantees.
Contribution
It introduces a novel deterministic bound on the restricted isometry constant based on frequency sets, improving RIP analysis for sketching operators.
Findings
New deterministic bounds on RIP constants
Concentration inequalities for random sketching operators
Theoretical guarantees for structured sketching methods
Abstract
In the context of sketching for compressive mixture modeling, we revisit existing proofs of the Restricted Isometry Property of sketching operators with respect to certain mixtures models. After examining the shortcomings of existing guarantees, we propose an alternative analysis that circumvents the need to assume importance sampling when drawing random Fourier features to build random sketching operators. Our analysis is based on new deterministic bounds on the restricted isometry constant that depend solely on the set of frequencies used to define the sketching operator; then we leverage these bounds to establish concentration inequalities for random sketching operators that lead to the desired RIP guarantees. Our analysis also opens the door to theoretical guarantees for structured sketching with frequencies associated to fast random linear operators.
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsSparse and Compressive Sensing Techniques · RNA Research and Splicing · Mathematical Approximation and Integration
MethodsSparse Evolutionary Training
