Abstract
This lecture overviews Graph-Based Pattern Recognition that has many applications in data clustering and dimensionality reduction. It covers the following topics in detail: Graph-based Clustering, Locality Preserving Projections, Locally Linear Embedding, ISOMAP, Laplacian Embedding, Linear Discriminant Analysis, Marginal Fisher Analysis, Local Fisher Discriminant Analysis, Semi-supervised Discriminant Analysis, Laplacian Support Vector Machines.
k-nearest neighbors graph.
Graph-based Clustering.
Graph-Based-Pattern-Recognition-v1.1-Summary