Description
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
Table of Contents
Spectral Embedding Methods for Manifold Learning. Robust Laplacian Eigenmaps Using Global Information. Density Preserving Maps. Sample Complexity in Manifold Learning. Manifold Alignment. Large-scale Manifold Learning. Metric and Heat Kernel. Discrete Ricci Flow for Surface and 3-Manifold. 2D and 3D Objects Morphing Using Manifold Techniques. Learning Image Manifolds from Local Features. Human Motion Analysis Applications of Manifold Learning.
