Density Peak Hashing
|International Journal of Computer Science and Engineering|
|© 2020 by SSRG - IJCSE Journal|
|Volume 7 Issue 8|
|Year of Publication : 2020|
|Authors : Zhen Wang, Fuzhen Sun, Baomin Shao|
How to Cite?
Zhen Wang, Fuzhen Sun, Baomin Shao, "Density Peak Hashing," SSRG International Journal of Computer Science and Engineering , vol. 7, no. 8, pp. 1-7, 2020. Crossref, https://doi.org/10.14445/23488387/IJCSE-V7I8P101
Hashing algorithms can map the floating-point data into compact binary code, and it can fast respond to the ANN search task according to the Hamming distance. The main idea of hashing algorithms is to cluster the data points into different groups and assign binary codes. Generally, many
existing hashing algorithms adopt the K-means clustering algorithm to divide the data points into different clusters. K-means clustering algorithm learns the clustering results according to the Euclidean distances among data points, and clusters the data points with small distances to a center into
the same group. Therefore, the K-means clustering algorithm is not applicable to the data with the nonspherical distribution. To solve this problem, this
paper proposes to compute the clustering groups based on density peaks and learns the binary codes according to the obtained cluster groups, which can make the encoding results well adaptive to data distribution. Furthermore, a two-step mechanism is adopted to learn the linear hashing functions which can recompute the above binary encoding results. To effectively reduce the training time complexity in this paper, only cluster centers are involved in the training process. While learning the hashing functions, the cluster centers’ binary codes are demanded to preserve the Hamming space's
Euclidean similarity relationship. Thus, the data pairs’ Hamming distances can approximate their Euclidean distance. The comparative ANN search experiments in three image datasets show that the proposed density peak hashing (DPH) can achieve an excellent performance.
Hashing algorithms, Approximate nearest neighbor search, Density peak, Binary code
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