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Operations Research Transactions >

2017 , Vol. 21 >Issue 2: 31 - 38

DOI: https://doi.org/10.15960/j.cnki.issn.1007-6093.2017.02.004

High-dimensional constrained matrix regression problems

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  • 1. School of Science, Beijing Jiaotong University, Beijing 100044, China 2.  School of Mathematics, University of Southampton, Highfield  SO17 1BJ,  Southampton, UK

Received date: 2017-03-24

  Online published: 2017-06-15

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Abstract

High-dimensional constrained matrix regression refers to non-convex constrained statistical regression with the multivariate responses and multivariate predictors in the high-dimensional setting. Its mathematical model is a matrix optimization, which is generally NP-hard and has a wide range of applications in a lot of areas such as machine learning and artificial intelligence, medical imaging and diagnosis, gene expression analysis, neural networks, risk management. This paper briefly reviews the new results on optimization theory and algorithm of high-dimensional constrained matrix regression. Moreover, we list the corresponding important references.

Key words： matrix regression; non-convex constraint; matrix optimization; optimization theory; optimization algorithm

Cite this article

KONG Lingchen, CHEN Bingzhen, XIU Naihua, QI Houduo . High-dimensional constrained matrix regression problems[J]. Operations Research Transactions, 2017 , 21(2) : 31 -38 . DOI: 10.15960/j.cnki.issn.1007-6093.2017.02.004

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