Sparse recovery: from vectors to tensors

• 来源期刊: 国家科学评论（英文版）
• 关键词: high-dimensional data sparsity compressive sensing low-rank matrix recovery tensors
• 年，卷（期）: 2018,（5）
• 页码范围: 756-767
• 页数: 12页
• 语种: 英文
• DOI: 10.1093/nsr/nwx069

摘要

Recent advances in various fields such as telecommunications,biomedicine and economics,among others,have created enormous amount of data that are often characterized by their huge size and high dimensionality.It has become evident,from research in the past couple of decades,that sparsity is a flexible and powerful notion when dealing with these data,both from empirical and theoretical viewpoints.In this survey,we review some of the most popular techniques to exploit sparsity,for analyzing high-dimensional vectors,matrices and higher-order tensors.