👉 Loc (Locality Sensitivity Mapping) is a non-linear dimensionality reduction technique that focuses on preserving the distances between data points in high-dimensional spaces, making it particularly effective for tasks involving similarity searches, such as image retrieval or text matching. It works by constructing a metric space where the distances between points are maintained as accurately as possible through a process of iterative mapping and optimization. Loc maps high-dimensional data points into a lower-dimensional space (typically 2D or 3D) while ensuring that the distances between nearby points in the original space are preserved as closely as possible in the reduced space. This makes Loc computing invaluable for applications where understanding and leveraging the proximity of data points is crucial, such as in recommendation systems, bioinformatics, and computer vision.
