Vector Retrieval

This chapter sets the stage for the remainder of this monograph. It explains where vectors come from, how they have come to represent data of any modality, and why they are a useful mathematical tool in machine learning. It then describes the structure we typically expect from a collection of vectors: that similar objects get vector representations that are close to each other in an inner product or metric space. We then define the problem of top-k retrieval over a well-structured collection of vectors, and explore its different flavors, including approximate retrieval.