Talks and Poster Presentations (with Proceedings-Entry):

G. Kail, S. Chepuri, G. Leus:
"Robust Censoring for Linear Inverse Problems";
Poster: IEEE 16th International Workshop on Signal Processing Advances in Wireless Communications (SPAWC 2015), Stockholm, Sweden; 06-28-2015 - 07-01-2015; in: "IEEE 16th International Workshop on Signal Processing Advances in Wireless Communications, 2015. SPAWC 2015.", IEEE, (2015), 495 - 499.

English abstract:
Existing methods for smart data reduction are typically sensitive to outlier data that do not follow postulated data models. We propose robust censoring as a joint approach unifying the concepts of robust learning and data censoring. We focus on linear inverse problems and formulate robust censoring through a sparse sensing operator, which is a non-convex bilinear problem. We propose two solvers, one using alternating descent and the other using Metropolis-Hastings sampling. Although the latter is based on the concept of Bayesian sampling, we avoid confining the outliers to a specific model. Numerical results show that the proposed Metropolis-Hastings sampler outperforms state-of-the-art robust estimators.

Robustness, censoring, sparse sensing, big data

Electronic version of the publication:

Created from the Publication Database of the Vienna University of Technology.