PANORAMA is a machine learning-based approach proposed to address the validation bottleneck of Approximate Nearest-Neighbor Search (ANNS), which efficiently finds data close to a given query in high-dimensional spaces. It facilitates cumulative improvement of the distance boundary by learning an orthogonal transformation through data-adaptive learning. This compresses more than 90% of the signal energy into the first dimension, enabling early candidate pruning through partial distance calculations. PANORAMA has been integrated into state-of-the-art ANNS methods such as IVFPQ/Flat, HNSW, MRPT, and Annoy, achieving speedups of 20-30x.
