Near-Optimal Hashing Algorithms for Approximate Nearest Neighbor in High Dimensions
FOCS '06 Proceedings of the 47th Annual IEEE Symposium on Foundations of Computer Science
Self-taught hashing for fast similarity search
Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval
Laplacian co-hashing of terms and documents
ECIR'2010 Proceedings of the 32nd European conference on Advances in Information Retrieval
Kernelized Locality-Sensitive Hashing
IEEE Transactions on Pattern Analysis and Machine Intelligence
Machine Learning for Computer Vision
Machine Learning for Computer Vision
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Spectral hashing (SpH) is an efficient and simple binary hashing method, which assumes that data are sampled from a multidimensional uniform distribution. However, this assumption is too restrictive in practice. In this paper we propose an improved method, Fitted Spectral Hashing, to relax this distribution assumption. Our work is based on the fact that one-dimensional data of any distribution could be mapped to a uniform distribution without changing the local neighbor relations among data items. We have found that this mapping on each PCA direction has certain regular pattern, and could fit data well by S-Curve function, Sigmoid function. With more parameters Fourier function also fit data well. Thus with Sigmoid function and Fourier function, we propose two binary hashing methods. Experiments show that our methods are efficient and outperform state-of-the-art methods.