Graph PCA Hashing for Similarity Search
This paper proposes a new hashing framework to conduct similarity search via the following steps: first, employing linear clustering methods to obtain a set of representative data points and a set of landmarks of the big dataset; second, using the landmarks to generate a probability representation for each data point. The proposed probability representation method is further proved to preserve the neighborhood of each data point. Third, PCA is integrated with manifold learning to lean the hash functions using the probability representations of all representative data points. As a consequence, the proposed hashing method achieves efficient similarity search (with linear time complexity) and effective hashing performance and high generalization ability (simultaneously preserving two kinds of complementary similarity structures, i.e., local structures via manifold learning and global structures via PCA). Experimental results on four public datasets clearly demonstrate the advantages of our proposed method in terms of similarity search, compared to the state-of-the-art hashing methods.