Improving LSH via Tensorized Random Projection

Locality sensitive hashing (LSH) is a fundamental algorithmic toolkit used by data scientists for approximate nearest neighbour search problems that have been used extensively in many large scale data processing applications such as near duplicate detection, nearest neighbour search, clustering, etc. In this work, we aim to propose faster and space efficient locality sensitive hash functions for Euclidean distance and cosine similarity for tensor data. Typically, the naive approach for obtaining LSH for tensor data involves first reshaping the tensor …

aim applications clustering cs.ds cs.lg data data processing data scientists detection duplicate etc faster hashing lsh near processing projection random scale scientists search space stat.ml toolkit via work