Efficient Graph Learning From Noisy and Incomplete Data

You are here

Top Reasons to Join SPS Today!

1. IEEE Signal Processing Magazine
2. Signal Processing Digital Library*
3. Inside Signal Processing Newsletter
4. SPS Resource Center
5. Career advancement & recognition
6. Discounts on conferences and publications
7. Professional networking
8. Communities for students, young professionals, and women
9. Volunteer opportunities
10. Coming soon! PDH/CEU credits
Click here to learn more.

Efficient Graph Learning From Noisy and Incomplete Data

By: 
Peter Berger; Gabor Hannak; Gerald Matz

We consider the problem of learning a graph from a given set of smooth graph signals. Our graph learning approach is formulated as a constrained quadratic program in the edge weights. We provide an implicit characterization of the optimal solution and propose a tailored ADMM algorithm to solve this problem efficiently. Several nearest neighbor and smoothness based graph learning methods are shown to be special cases of our approach. Specifically, our algorithm yields an efficient but extremely accurate approximation to b-matched graphs. We then propose a generalization of our scheme that can deal with noisy and incomplete data via joint graph learning and signal inpainting. We compare the performance of our approach with state-of-the art methods on synthetic data and on real-world data from the Austrian National Council.

SPS on Twitter

SPS Videos


Signal Processing in Home Assistants

 


Multimedia Forensics


Careers in Signal Processing             

 


Under the Radar