Statistics / Statistical Learning & Data Mining
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 reduc-
tion, nds widespread application in machine learning, neural networks, pattern rec-
ognition, 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, semidenite 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, and offers a rich set of specialized topics contributed
by expert professionals and researchers from a variety of elds. Finally, the book
discusses specic algorithms and methodologies using case studies to apply manifold
learning for real-world problems.
ISBN: 978-1-4398-7109-6
9 781439 871096
90000
w w w . c r c p r e s s . c o m
www.c rcp res s. co m
K13255
Manifold Learning
Theory and Applicaons
Manifold Learning
Theory and Applicaons
Yunqian Ma and Yun Fu
Ma • Fu
Manifold Learning Theory and Applicaons
K13255 cvr mech.indd 1 11/15/11 10:50 AM