Kernelized Locality-Sensitive Hashing for Semi-Supervised Agglomerative ClusteringDownload PDF

29 Mar 2024 (modified: 17 Jan 2013)ICLR 2013 conference submissionReaders: Everyone
Decision: reject
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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