Multilinear subspace learning : dimensionality reduction of multidimensional data /

"Due to advances in sensor, storage, and networking technologies, data is being generated on a daily basis at an ever-increasing pace in a wide range of applications, including cloud computing, mobile Internet, and medical imaging. This large multidimensional data requires more efficient dimens...

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Détails bibliographiques
Cote:Libro Electrónico
Auteurs principaux: Lu, Haiping (Auteur), Plataniotis, Konstantinos N. (Auteur), Venetsanopoulos, A. N. (Anastasios N.), 1941- (Auteur)
Format: Électronique eBook
Langue:Inglés
Publié: Boca Raton, FL : CRC Press/Taylor and Francis Group, [2014]
Collection:Chapman & Hall/CRC machine learning & pattern recognition series.
Sujets:
Data compression (Computer science)
Big data.
Multilinear algebra.
Données > Compression (Informatique)
Données volumineuses.
COMPUTERS > Database Management > Data Mining.
COMPUTERS > Machine Theory.
TECHNOLOGY & ENGINEERING > Electrical.
Big data
Multilinear algebra
Accès en ligne:Texto completo
Table des matières:
  • Introduction
  • Fundamentals and Foundations
  • Linear Subspace Learning for Dimensionality Reduction
  • Fundamentals of Multilinear Subspace Learning
  • Overview of Multilinear Subspace Learning
  • Algorithmic and Computational Aspects
  • Algorithms and Applications
  • Multilinear Principal Component Analysis
  • Multilinear Discriminant Analysis
  • Multilinear ICA, CCA, and PLS
  • Applications of Multilinear Subspace Learning
  • Appendix A: Mathematical Background
  • Appendix B: Data and Preprocessing
  • Appendix C: Software.

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