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A survey of multilinear subspace learning for tensor data

8 years 1 months ago
A survey of multilinear subspace learning for tensor data
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. Key words: Subspace learning, dimensionality reduction, feature extraction, multidimensional data, tensor, multilinear, survey, taxonomy Please...
Haiping Lu, Konstantinos N. Plataniotis, Anastasio
Added 17 Sep 2011
Updated 17 Sep 2011
Type Journal
Year 2011
Where PR
Authors Haiping Lu, Konstantinos N. Plataniotis, Anastasios N. Venetsanopoulos
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