Journal
PATTERN RECOGNITION
Volume 44, Issue 7, Pages 1540-1551Publisher
ELSEVIER SCI LTD
DOI: 10.1016/j.patcog.2011.01.004
Keywords
Subspace learning; Dimensionality reduction; Feature extraction; Multidimensional data; Tensor; Multilinear; Survey; Taxonomy
Ask authors/readers for more resources
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.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available