Thursday, October 24, 2013

Manifold Learning Theory and Applications

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Manifold Learning Theory and Applications Title: Manifold Learning Theory and Applications
Author: Yun Fu, Yunqian Ma
Publisher: 2011-12-20
Format: Online PDF (314 pages, 9.1MB)
Language: English
ISBN-10: 1439871094
ISBN-13: 9781439871096

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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.

Table of Contents

Chapter 1. Spectral Embedding Methods for Manifold Learning
Chapter 2. Robust Laplacian Eigenmaps Using Global Information
Chapter 3. Density Preserving Maps
Chapter 4. Sample Complexity in Manifold Learning
Chapter 5. Manifold Alignment
Chapter 6. Large-Scale Manifold Learning
Chapter 7. Metric and Heat Kernel
Chapter 8. Discrete Ricci Flow for Surface and 3-Manifold
Chapter 9. 2D and 3D Objects Morphing Using Manifold Techniques
Chapter 10. Learning Image Manifolds from Local Features
Chapter 11. Human Motion Analysis Applications of Manifold Learning

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