A Review on Low-Rank Models in Data Analysis

Zhouchen Lin; Zhouchen Lin

doi:10.3934/bdia.2016001

Big Data and Information Analytics

2016, Volume 1, Issue 2: 139-161. doi: 10.3934/bdia.2016001

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A Review on Low-Rank Models in Data Analysis

Zhouchen Lin

1.
Key Lab. of Machine Perception (MOE), School of EECS Peking University, Beijing, China;
2.
Cooperative Medianet Innovation Center Shanghai Jiao Tong University, Shanghai, China

Received: 01 October 2015 Revised: 01 December 2016 Published: 01 July 2016

Nowadays we are in the big data era. The high-dimensionality of data imposes big challenge on how to process them effectively and efficiently. Fortunately, in practice data are not unstructured. Their samples usually lie around low-dimensional manifolds and have high correlation among them. Such characteristics can be effectively depicted by low rankness. As an extension to the sparsity of first order data, such as voices, low rankness is also an effective measure for the sparsity of second order data, such as images. In this paper, I review the representative theories, algorithms and applications of the low rank subspace recovery models in data processing.

Keywords:

Citation: Zhouchen Lin. A Review on Low-Rank Models in Data Analysis[J]. Big Data and Information Analytics, 2016, 1(2): 139-161. doi: 10.3934/bdia.2016001

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Abstract

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