A survey of multilinear subspace learning for tensor data

  • Authors:
  • Haiping Lu;Konstantinos N. Plataniotis;Anastasios N. Venetsanopoulos

  • Affiliations:
  • Institute for Infocomm Research, Agency for Science, Technology and Research, #21-01 Connexis (South Tower), 1 Fusionopolis Way, Singapore 138632, Singapore;The Edward S. Rogers Sr. Department of Electrical and Computer Engineering, University of Toronto, 10 King's College Road, Toronto, ON, Canada M5A 3G4;The Edward S. Rogers Sr. Department of Electrical and Computer Engineering, University of Toronto, 10 King's College Road, Toronto, ON, Canada M5A 3G4 and Department of Electrical and Computer Eng ...

  • Venue:
  • Pattern Recognition
  • Year:
  • 2011

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Abstract

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.