Graph-Based Pattern Recognition Lecture
Nowadays, Artificial Intelligence drives scientific and economic growth worldwide. This is largely due to advances in Machine Learning (ML). Its applications span and revolutionize almost every human activity:
-Autonomous Systems (cars, drones, vessels),
-Media Content and Art Creation (including fake data creation/detection), Social Media Analytics,
-Medical Imaging and Diagnosis,
-Financial Engineering (forecasting and analytics), Big Data Analytics,
-Broadcasting, Internet and Communications,
-Robotics/Control
-Intelligent Human-Machine Interaction, Anthropocentric (human-centered) Computing,
-Smart Cities/Buildings and Assisted living.
-Scientific Modeling and Analytics.
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.