Label Propagation 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 Label Propagation that has many applications in pattern recognition (semi-supervised learning) and in the study of diffusion processes. It covers the following topics in detail: Graph construction approaches (Adjacency Matrix Construction, Graph Weighting, Simultaneous Graph Construction and Weighting). Label Inference Methods (Graph Min-cut, Markov Random Fields, Gaussian Random Fields, Local and Global Consistency, Label Propagation on Data with Multiple Representations, Label Propagation on Hypergraphs). Label Propagation for Deep Learning.