Also look at Semantic Hashing, which uses deep learning to compress an input (e.g. a document) into a low dimensional bit vector with fast lookup: http://www.c

Also look at Semantic Hashing, which uses deep learning to compress an input (e.g. a document) into a low dimensional bit vector with fast lookup: http://www.cs.toronto.edu/~rsalakhu/papers/semantic_final.pdf http://www.cs.toronto.edu/~rsalakhu/papers/semantic_final.pd... The idea is that you could hash one billion objects, and do a very fast lookup of similar objects. There is later academic work that refines this approach. For example, here is work applying it to image search: http://www.cs.utexas.edu/~grauman/temp/GraumanFergus_Hashing_chapterdraft.pdf http://www.cs.utexas.edu/~grauman/temp/GraumanFergus_Hashing... You can consider this a variant of Locality-Sensitive Hashing, where the hash function is specifically induced through a machine learning technique.