  Nearest neighbors search is a fundamental problem in various research fields
like machine learning, data mining and pattern recognition. Recently,
hashing-based approaches, e.g., Locality Sensitive Hashing (LSH), are proved to
be effective for scalable high dimensional nearest neighbors search. Many
hashing algorithms found their theoretic root in random projection. Since these
algorithms generate the hash tables (projections) randomly, a large number of
hash tables (i.e., long codewords) are required in order to achieve both high
precision and recall. To address this limitation, we propose a novel hashing
algorithm called {\em Density Sensitive Hashing} (DSH) in this paper. DSH can
be regarded as an extension of LSH. By exploring the geometric structure of the
data, DSH avoids the purely random projections selection and uses those
projective functions which best agree with the distribution of the data.
Extensive experimental results on real-world data sets have shown that the
proposed method achieves better performance compared to the state-of-the-art
hashing approaches.
