Trained to extract actionable information from large volumes of
high-dimensional data, engineers and scientists often have trouble
isolating meaningful low-dimensional structures hidden in their
high-dimensional observations. Manifold learning, a groundbreaking
technique designed to tackle these issues of dimensionality
reduction, finds widespread application in machine learning, neural
networks, pattern recognition, image processing, and computer
vision. Filling a void in the literature, Manifold Learning Theory
and Applications incorporates state-of-the-art techniques in
manifold learning with a solid theoretical and practical treatment
of the subject. Comprehensive in its coverage, this pioneering work
explores this novel modality from algorithm creation to successful
implementation-offering examples of applications in medical,
biometrics, multimedia, and computer vision. Emphasizing
implementation, it highlights the various permutations of manifold
learning in industry including manifold optimization, large scale
manifold learning, semidefinite programming for embedding, manifold
models for signal acquisition, compression and processing, and
multi scale manifold. Beginning with an introduction to manifold
learning theories and applications, the book includes discussions
on the relevance to nonlinear dimensionality reduction, clustering,
graph-based subspace learning, spectral learning and embedding,
extensions, and multi-manifold modeling. It synergizes cross-domain
knowledge for interdisciplinary instructions, offers a rich set of
specialized topics contributed by expert professionals and
researchers from a variety of fields. Finally, the book discusses
specific algorithms and methodologies using case studies to apply
manifold learning for real-world problems.
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