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

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

By: 
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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