Discriminative Prototype Selection for Graph Embedding

Discriminative Prototype Selection for Graph Embedding

Explore graph embedding techniques and prototype selection methods in this 54-minute guest presentation by Massimo Piccardi from the University of Central Florida. Delve into key concepts including graph matching, graph edit distance, and bipartite graph edit distance. Learn about prototype-based graph embedding and various discriminative prototype selection approaches, such as center, border, repelling, spanning, and targetsphere selections. Examine experimental results comparing discriminative and conventional methods across different datasets, and understand the impact of prototype numbers per class. Gain valuable insights into graph theory and its applications in machine learning and data analysis.