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Top-k Similarity Search over Gaussian Distributions Based on KL-Divergence
2016
Journal of Information Processing
The problem of similarity search is a crucial task in many real-world applications such as multimedia databases, data mining, and bioinformatics. In this work, we investigate the similarity search on uncertain data modeled in Gaussian distributions. By employing Kullback-Leibler divergence (KL-divergence) to measure the dissimilarity between two Gaussian distributions, our goal is to search a database for the top-k Gaussian distributions similar to a given query Gaussian distribution.
doi:10.2197/ipsjjip.24.152
fatcat:4b7w7yml5rcn3oxfefgxo5r4mm