1. Home
2. Premium
3. IEEE ICASSP 2020 Virtual Conference May 2020
4. Generative Adversarial Networks For Graph Data Imputation From Signed Observations

Generative Adversarial Networks For Graph Data Imputation From Signed Observations

Collection:

IEEE ICASSP 2020 Virtual Conference May 2020

We study the problem of missing data imputation for graph signals from signed one-bit quantized observations. More precisely, we consider that the true graph data is drawn from a distribution of signals that are smooth or bandlimited on a known graph. How

• IEEE MemberUS $11.00
• Society MemberUS $0.00
• IEEE Student MemberUS $11.00
• Non-IEEE MemberUS $15.00

Purchase

Videos in this product

Generative Adversarial Networks For Graph Data Imputation From Signed Observations

00:12:47

0 views

We study the problem of missing data imputation for graph signals from signed one-bit quantized observations. More precisely, we consider that the true graph data is drawn from a distribution of signals that are smooth or bandlimited on a known graph. How