a latent space, is an embedding of a set of items within a manifold in which items resembling each other are positioned closer to one another. position within the latent space can be viewed as being defined by a set of latent variables. it encodes a meaningful internal representation of externally observed events.
it is an abstract, lower-dimensional representation of high-dimensional data, often used in machine learning and data science to simplify complex data structures and reveal hidden patterns. it is particularly useful in unsupervised learning techniques, such as dimensionality reduction, clustering, and generative modeling. by transforming data into a latent space, data scientists can more efficiently analyze, visualize, and manipulate the data, leading to improved model performance and interpretability.