A Hashing Method Using PCA-based Clustering


KIPS Transactions on Software and Data Engineering, Vol. 3, No. 6, pp. 215-218, Jun. 2014
10.3745/KTSDE.2014.3.6.215,   PDF Download:

Abstract

In hashing-based methods for approximate nearest neighbors(ANN) search, by mapping data points to k-bit binary codes, nearest neighbors are searched in a binary embedding space. In this paper, we present a hashing method using a PCA-based clustering method, Principal Direction Divisive Partitioning(PDDP). PDDP is a clustering method which repeatedly partitions the cluster with the largest variance into two clusters by using the first principal direction. The proposed hashing method utilizes the first principal direction as a projective direction for binary coding. Experimental results demonstrate that the proposed method is competitive compared with other hashing methods.


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Cite this article
[IEEE Style]
C. H. Park, "A Hashing Method Using PCA-based Clustering," KIPS Transactions on Software and Data Engineering, vol. 3, no. 6, pp. 215-218, 2014. DOI: 10.3745/KTSDE.2014.3.6.215.

[ACM Style]
Cheong Hee Park. 2014. A Hashing Method Using PCA-based Clustering. KIPS Transactions on Software and Data Engineering, 3, 6, (2014), 215-218. DOI: 10.3745/KTSDE.2014.3.6.215.