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Manifold Learning Theory and Applications by [edited by] Yunqian Ma, Yun Fu is a nonfiction available to read on EtoBox.

What is Manifold Learning Theory and Applications about?

<p>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.</p> <p>Filling a void in the literature, <b>Manifold Learning Theory and Applications</b> 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 manifol

Who reads Manifold Learning Theory and Applications?

Author

[edited by] Yunqian Ma, Yun Fu

Publisher

CRC Press LLC

Published

2011

Language

EN

ISBN

9780429251368

Category

nonfiction

Subjects

Business, Science, Mathematics