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Weighted hashing for fast large scale similarity search
ABSTRACT Similarity search, or finding approximate nearest neighbors, is an important technique for many applications. Many recent research demonstrate that hashing methods can achieve promising results for large scale similarity search due to its computational and memory efficiency. However, most existing hashing methods treat all hashing bits equally and the distance between data examples is calculated as the Hamming distance between their hashing codes, while different hashing bits may carry different amount of information. This paper proposes a novel method, named Weighted Hashing (WeiHash), to assign different weights to different hashing bits. The hashing codes and their corresponding weights are jointly learned in a unified framework by simultaneously preserving the similarity between data examples and balancing the variance of each hashing bit. An iterative coordinate descent optimization algorithm is designed to derive desired hashing codes and weights. Extensive experiments on two large scale datasets demonstrate the superior performance of the proposed research over several state-of-the-art hashing methods
Hashing for Similarity Search: A Survey
Similarity search (nearest neighbor search) is a problem of pursuing the data
items whose distances to a query item are the smallest from a large database.
Various methods have been developed to address this problem, and recently a lot
of efforts have been devoted to approximate search. In this paper, we present a
survey on one of the main solutions, hashing, which has been widely studied
since the pioneering work locality sensitive hashing. We divide the hashing
algorithms two main categories: locality sensitive hashing, which designs hash
functions without exploring the data distribution and learning to hash, which
learns hash functions according the data distribution, and review them from
various aspects, including hash function design and distance measure and search
scheme in the hash coding space
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