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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