Haiping Lu, Konstantinos N. Plataniotis, Anastasios Venetsanopoulos

Multilinear Subspace Learning (eBook, PDF)

Dimensionality Reduction of Multidimensional Data

• Format: PDF

Produktbeschreibung

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 dimensionality reduction schemes than the traditional techniqu

---

Dieser Download kann aus rechtlichen Gründen nur mit Rechnungsadresse in A, B, BG, CY, CZ, D, DK, EW, E, FIN, F, GR, HR, H, IRL, I, LT, L, LR, M, NL, PL, P, R, S, SLO, SK ausgeliefert werden.

Produktdetails

• Verlag: Taylor & Francis eBooks
• Seitenzahl: 296
• Erscheinungstermin: 11. Dezember 2013
• Englisch
• ISBN-13: 9781439857298
• Artikelnr.: 40613595

Autorenporträt

Haiping Lu, Konstantinos N. Plataniotis, Anastasios Venetsanopoulos

Inhaltsangabe

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. Appendices. Bibliography.
Index.