A survey of multilinear subspace learning for tensor data
A survey of multilinear subspace learning for tensor data
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DOI：
10.1016/j.patcog.2011.01.004
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发表时间：
2011-07-01
影响因子：
8
通讯作者：
Venetsanopoulos, Anastasios N.
中科院分区：
文献类型：
--
作者：
Lu, Haiping;Plataniotis, Konstantinos N.;Venetsanopoulos, Anastasios N.
在许多应用程序中，每天都会产生越来越多的多维数据。这导致了对学习算法的强烈需求，以便从这些海量数据中提取有用的信息。本文综述了直接从张量表示进行多维数据降维的多线性子空间学习(MSL)领域。它讨论了MSL的中心问题，包括通过多线性投影建立领域的基础，建立用于系统处理问题的统一的MSL框架，检查典型MSL解决方案的算法方面，以及将无监督和有监督的MSL算法归类到分类中。最后，本文总结了移动学习的广泛应用，并对未来的研究方向进行了展望。(C)2011爱思唯尔有限公司。保留所有权利。
Increasingly large amount of multidimensional data are being generated on a daily basis in many applications. This leads to a strong demand for learning algorithms to extract useful information from these massive data. This paper surveys the field of multilinear subspace learning (MSL) for dimensionality reduction of multidimensional data directly from their tensorial representations. It discusses the central issues of MSL, including establishing the foundations of the field via multilinear projections, formulating a unifying MSL framework for systematic treatment of the problem, examining the algorithmic aspects of typical MSL solutions, and categorizing both unsupervised and supervised MSL algorithms into taxonomies. Lastly, the paper summarizes a wide range of MSL applications and concludes with perspectives on future research directions. (c) 2011 Elsevier Ltd. All rights reserved.