论文标题
哈希学习与超级代表
Hashing Learning with Hyper-Class Representation
论文作者
论文摘要
现有的无监督哈希学习是一种以属性为中心的计算。它可能无法准确保留数据之间的相似性。这导致降低哈希功能学习的性能。在本文中，提出了一种用超级表示的哈希算法。这是两步方法。第一步找到潜在的决策特征并建立超级级别。第二步构造了基于第一步的超级级信息的哈希学习，因此，超级级别的数据的哈希码尽可能相似，并且超级级别之间的数据的哈希码尽可能不同。为了评估效率，在四个公共数据集上进行了一系列实验。实验结果表明，就平均平均精度（MAP），平均精度（AP）和Hamming Radius 2（HAM2）而言，所提出的哈希算法比比较算法更有效。
Existing unsupervised hash learning is a kind of attribute-centered calculation. It may not accurately preserve the similarity between data. This leads to low down the performance of hash function learning. In this paper, a hash algorithm is proposed with a hyper-class representation. It is a two-steps approach. The first step finds potential decision features and establish hyper-class. The second step constructs hash learning based on the hyper-class information in the first step, so that the hash codes of the data within the hyper-class are as similar as possible, as well as the hash codes of the data between the hyper-classes are as different as possible. To evaluate the efficiency, a series of experiments are conducted on four public datasets. The experimental results show that the proposed hash algorithm is more efficient than the compared algorithms, in terms of mean average precision (MAP), average precision (AP) and Hamming radius 2 (HAM2)
