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 techniques. Addressing this need, multilinear subspace
learning (MSL) reduces the dimensionality of big data directly from
its natural multidimensional representation, a tensor.
Multilinear Subspace Learning: Dimensionality Reduction of
Multidimensional Data gives a comprehensive introduction to both
theoretical and practical aspects of MSL for the dimensionality
reduction of multidimensional data based on tensors. It covers the
fundamentals, algorithms, and applications of MSL.
Emphasizing essential concepts and system-level perspectives,
the authors provide a foundation for solving many of today s most
interesting and challenging problems in big multidimensional data
processing. They trace the history of MSL, detail recent advances,
and explore future developments and emerging applications.
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. Implementation tips help practitioners in further
development, evaluation, and application. The book also provides
researchers with useful theoretical information on big
multidimensional data in machine learning and pattern recognition.
MATLAB(r) source code, data, and other materials are available at
www.comp.hkbu.edu.hk/ haiping/MSL.html"
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