Efficient approximate similarity search using random projection learning

Efficient similarity search on high dimensional data is an important research topic in database and information retrieval fields. In this paper, we propose a random projection learning approach for solving the approximate similarity search problem. First, the random projection technique of the locality sensitive hashing is applied for generating the high quality binary codes. Then the binary code is treated as the labels and a group of SVM classifiers are trained with the labeled data for predicting the binary code for the similarity queries. The experiments on real datasets demonstrate that our method substantially outperforms the existing work in terms of preprocessing time and query processing.