Haiping Lu, Konstantinos N Plataniotis, Anastasios Venetsanopoulos

Multilinear Subspace Learning

Dimensionality Reduction of Multidimensional Data

• Gebundenes Buch

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.

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Produktdetails

• Verlag: Taylor & Francis Ltd (Sales)
• Seitenzahl: 296
• Erscheinungstermin: 11. Dezember 2013
• Englisch
• Abmessung: 236mm x 157mm x 23mm
• Gewicht: 590g
• ISBN-13: 9781439857243
• ISBN-10: 1439857245
• Artikelnr.: 32933759

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.