学术文献库

In the era of big data, methods for improving memory and computational efficiency have become crucial for successful deployment of technologies. Hashing is one of the most effective approaches to deal with computational limitations that come with big data. One natural way for formulating this problem is spectral hashing that directly incorporates affinity to learn binary codes. However, due to binary constraints, the optimization becomes intractable. To mitigate this challenge, different relaxation approaches have been proposed to reduce the computational load of obtaining binary codes and still attain a good solution. The problem with all existing relaxation methods is resorting to one or more additional auxiliary variables to attain high quality binary codes while relaxing the problem. The existence of auxiliary variables leads to coordinate descent approach which increases the computational complexity. We argue that introducing these variables is unnecessary. To this end, we propose a novel relaxed formulation for spectral hashing that adds no additional variables to the problem. Furthermore, instead of solving the problem in original space where number of variables is equal to the data points, we solve the problem in a much smaller space and retrieve the binary codes from this solution. This trick reduces both the memory and computational complexity at the same time. We apply two optimization techniques, namely projected gradient and optimization on manifold, to obtain the solution. Using comprehensive experiments on four public datasets, we show that the proposed efficient spectral hashing (ESH) algorithm achieves highly competitive retrieval performance compared with state of the art at low complexity.