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Kernelized Locality-Sensitive Hashing for Semi-Supervised Agglomerative Clustering
Boyi Xie, Shuheng Zheng
Jan 17, 2013 (modified: Jan 17, 2013)ICLR 2013 conference submissionreaders: everyone
Abstract:Large scale agglomerative clustering is hindered by computational burdens. We propose a novel scheme where exact inter-instance distance calculation is replaced by the Hamming distance between Kernelized Locality-Sensitive Hashing (KLSH) hashed values. This results in a method that drastically decreases computation time. Additionally, we take advantage of certain labeled data points via distance metric learning to achieve a competitive precision and recall comparing to K-Means but in much less computation time.
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