Vector Space

Vector space models represent data points as vectors in a multi-dimensional space, enabling the capture and analysis of relationships between data items. Current research focuses on extending vector space applications beyond traditional numerical data to encompass diverse inputs like text, images, and even mathematical expressions, often leveraging neural networks and graph-based methods for embedding and reasoning. These advancements are driving progress in various fields, including natural language processing, knowledge graph reasoning, and signal processing, by facilitating efficient information retrieval, improved model interpretability, and enhanced analytical capabilities. The development of robust and scalable vector space methods is crucial for handling increasingly complex datasets and advancing machine learning applications.