Improved Vectorization

Improved vectorization techniques aim to represent complex data, such as images or topological structures, as numerical vectors for efficient processing and analysis. Current research focuses on developing stable and informative vectorization methods for diverse data types, including those arising from computer vision, persistent homology, and dataset comparisons, employing approaches like adapting existing algorithms for new architectures (e.g., RISC-V) and creating novel representations such as bag-of-prototypes. These advancements enable faster computations, improved model performance, and more effective analysis of relationships between datasets, impacting fields ranging from machine learning to data science.