Haiping Lu, Konstantinos N. Plataniotis, Anastasios Venetsanopoulos

Multilinear Subspace Learning (eBook, PDF)

Dimensionality Reduction of Multidimensional Data

• Format: PDF

Produktbeschreibung

Emphasizing essential concepts and system-level perspectives, this book provides a foundation for solving many of today's most interesting and challenging problems in big multidimensional data processing. It gives a comprehensive introduction to both theoretical and practical aspects of MSL for the dimensionality reduction of multidimensional data based on tensors. The book follows a unifying MSL framework formulation to systematically derive representative MSL algorithms. It describes various applications of the algorithms, along with their pseudocode. Supporting materials are available online.

---

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
• 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.