Density Sensitive Hashing
This work addresses the scalability and accuracy challenges in nearest neighbor search for machine learning and data mining applications, representing an incremental improvement over existing hashing techniques.
The paper tackles the inefficiency of random projection in hashing-based nearest neighbor search by proposing Density Sensitive Hashing (DSH), which leverages data geometry to select better projections, resulting in improved performance over state-of-the-art methods on real-world datasets.
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.