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Books > Computing & IT > General theory of computing > Mathematical theory of computation
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Multivariate Data Analysis on Matrix Manifolds - (with Manopt) (Hardcover, 1st ed. 2021)
Loot Price: R1,240
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Multivariate Data Analysis on Matrix Manifolds - (with Manopt) (Hardcover, 1st ed. 2021)
Series: Springer Series in the Data Sciences
Expected to ship within 9 - 17 working days
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This graduate-level textbook aims to give a unified presentation
and solution of several commonly used techniques for multivariate
data analysis (MDA). Unlike similar texts, it treats the MDA
problems as optimization problems on matrix manifolds defined by
the MDA model parameters, allowing them to be solved using (free)
optimization software Manopt. The book includes numerous in-text
examples as well as Manopt codes and software guides, which can be
applied directly or used as templates for solving similar and new
problems. The first two chapters provide an overview and essential
background for studying MDA, giving basic information and
notations. Next, it considers several sets of matrices routinely
used in MDA as parameter spaces, along with their basic topological
properties. A brief introduction to matrix (Riemannian) manifolds
and optimization methods on them with Manopt complete the MDA
prerequisite. The remaining chapters study individual MDA
techniques in depth. The number of exercises complement the main
text with additional information and occasionally involve open
and/or challenging research questions. Suitable fields include
computational statistics, data analysis, data mining and data
science, as well as theoretical computer science, machine learning
and optimization. It is assumed that the readers have some
familiarity with MDA and some experience with matrix analysis,
computing, and optimization.
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