Locality-Sensitive Hashing with Margin Based Feature Selection
This work addresses similarity search and personal authentication challenges in domains like biometrics and multimedia, but it appears incremental as it builds on existing Locality-Sensitive Hashing techniques with a specific optimization approach.
The paper tackles the problem of optimizing Locality-Sensitive Hashing for similarity searches by proposing a learning method with feature selection that uses longer bit arrays and selects bits for use, demonstrating effectiveness on fingerprint images with many labels and few data per label, as well as on natural images, handwritten digits, and speech features.
We propose a learning method with feature selection for Locality-Sensitive Hashing. Locality-Sensitive Hashing converts feature vectors into bit arrays. These bit arrays can be used to perform similarity searches and personal authentication. The proposed method uses bit arrays longer than those used in the end for similarity and other searches and by learning selects the bits that will be used. We demonstrated this method can effectively perform optimization for cases such as fingerprint images with a large number of labels and extremely few data that share the same labels, as well as verifying that it is also effective for natural images, handwritten digits, and speech features.