Neighborhood preserving hashing for fast similarity search

Fast similarity search methods are increasingly critical for many large-scale learning tasks, particularly in the communities of machine learning and data mining. Recently, data-aware hashing method is regarded as a promising approach for similarity search which maps high-dimensional feature vectors into efficient and compact hash codes while preserving the corresponding neighborhood structure. Although some recent hashing methods based on eigenvalue decomposition perform well, they suffer from semantic loss. In this paper, we concentrate on this issue and propose a novel neighborhood preserving hashing approach which adopts a brand-new method to combine non-negative matrix factorization and locality linear embedding without introducing any additional parameter. The combination of these two classical techniques ensures that we obtain a parts-based representation which not only fulfill the psychological and physiological requirements of human perception but also conserve the intrinsic neighborhood structure of the original data. Experiments are conducted to demonstrate that the proposed approach is superior to some state-of-the-art methods.