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Principal Component Analysis Networks and Algorithms (Hardcover, 1st ed. 2017)
Loot Price: R4,922
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Principal Component Analysis Networks and Algorithms (Hardcover, 1st ed. 2017)
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This book not only provides a comprehensive introduction to
neural-based PCA methods in control science, but also presents many
novel PCA algorithms and their extensions and generalizations,
e.g., dual purpose, coupled PCA, GED, neural based SVD algorithms,
etc. It also discusses in detail various analysis methods for the
convergence, stabilizing, self-stabilizing property of algorithms,
and introduces the deterministic discrete-time systems method to
analyze the convergence of PCA/MCA algorithms. Readers should be
familiar with numerical analysis and the fundamentals of
statistics, such as the basics of least squares and stochastic
algorithms. Although it focuses on neural networks, the book only
presents their learning law, which is simply an iterative
algorithm. Therefore, no a priori knowledge of neural networks is
required. This book will be of interest and serve as a reference
source to researchers and students in applied mathematics,
statistics, engineering, and other related fields.
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