What Should We Learn? Special Issue on Exploiting Structure in Data Analytics in SPM

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News and Resources for Members of the IEEE Signal Processing Society

What Should We Learn? Special Issue on Exploiting Structure in Data Analytics in SPM

Yang Li

Individual feature articles and special issues are two major mechanisms of full-length tutorial surveys of IEEE Signal Processing Magazine (SPM). Since the May 2016 feature article cluster by Jane Wang et al. on brain signal analytics, SPM’s current and past editors-in-chief and their teams have been exploring a different way to complement this existing structure—a feature article cluster (or mini special issue) that allows for a set of three to five solicited articles on a current topic, instead of just one (feature article) or ten to 11 (a special issue). The special issue of SPM in July 2018 offers a feature article cluster on exploiting structure in data analytics: low-rank and sparse structures and is the second such cluster. It is the first in SPM’s planned yearly series on data science and includes the following four articles:

“Harnessing Structures in Big Data via Guaranteed Low-Rank Matrix Estimation” by Chen and Chi

“Robust Subspace Learning” by Vaswani et al.

“Correlation-Awareness in Low-Rank Models” by Pal

“Theoretical Foundations of Deep Learning via Sparse Representations” by Papyan et al.



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