ABSTRACT

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

chapter 3|16 pages

Density Preserving Maps

chapter 4|22 pages

Sample Complexity in Manifold Learning

chapter 5|26 pages

Manifold Alignment

chapter 6|24 pages

Large-Scale Manifold Learning

chapter 7|22 pages

Metric and Heat Kernel

chapter 10|20 pages

Learning Image Manifolds from Local Features