Feature Extraction and Visualization Methods in Topological Data Analysis

Watch a 54-minute lecture from the Applied Algebraic Topology Network exploring feature extraction and visualization methods in Topological Data Analysis (TDA). Delve into the two primary applications of TDA in applied sciences: generating multimodal data features for statistical and machine learning analyses, and creating visualization tools for exploring high-dimensional dataset structures. Learn about persistent homology and the Mapper algorithm, while understanding their inherent challenges - from computational complexity and multi-parameter generalization issues in persistent homology to parameter dependency and sensitivity concerns in the Mapper algorithm.