Manifold Learning Theory and Applications

Business & Finance, Economics, Statistics, Nonfiction, Computers, Database Management, Science & Nature, Mathematics
Cover of the book Manifold Learning Theory and Applications by , CRC Press
View on Amazon View on AbeBooks View on Kobo View on B.Depository View on eBay View on Walmart
Author: ISBN: 9781466558878
Publisher: CRC Press Publication: December 20, 2011
Imprint: CRC Press Language: English
Author:
ISBN: 9781466558878
Publisher: CRC Press
Publication: December 20, 2011
Imprint: CRC Press
Language: English

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.

View on Amazon View on AbeBooks View on Kobo View on B.Depository View on eBay View on Walmart

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.

More books from CRC Press

Cover of the book Ranking of Multivariate Populations by
Cover of the book Carpentry and Joinery 2, 3rd ed by
Cover of the book Wireless Technologies by
Cover of the book Drawn to Life: 20 Golden Years of Disney Master Classes Volume 1 by
Cover of the book Integrated IT Performance Management by
Cover of the book The Acoustics of Wood (1995) by
Cover of the book Computational Actuarial Science with R by
Cover of the book Interfacial Electrochemistry by
Cover of the book Microalgal Production for Biomass and High-Value Products by
Cover of the book Oceanography and Marine Biology by
Cover of the book Humanity in Healthcare by
Cover of the book GPU Pro 360 Guide to Rendering by
Cover of the book Mass Customized Manufacturing by
Cover of the book Bulk Metallic Glasses by
Cover of the book Mediterranean Foods by
We use our own "cookies" and third party cookies to improve services and to see statistical information. By using this website, you agree to our Privacy Policy