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Nearest Neighbors Graph Construction: Peer Sampling to the Rescue

Abstract : In this paper, we propose an efficient KNN service, called KPS (KNN-Peer-Sampling). The KPS service can be used in various contexts e.g. recommendation systems, information retrieval and data mining. KPS borrows concepts from P2P gossip-based clustering protocols to provide a localized and efficient KNN computation in large-scale systems. KPS is a sampling-based iterative approach, combining ran-domness, to provide serendipity and avoid local minimum, and clustering , to ensure fast convergence. We compare KPS against the state of the art KNN centralized computation algorithm NNDescent, on multiple datasets. The experiments confirm the efficiency of KPS over NNDescent: KPS improves significantly on the computational cost while converging quickly to a close to optimal KNN graph. For instance, the cost, expressed in number of pairwise similarity computations, is reduced by ≈ 23% and ≈ 49% to construct high quality KNN graphs for Jester and MovieLens datasets, respectively. In addition, the randomized nature of KPS ensures eventual convergence, not always achieved with NNDescent.
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Contributor : Javier Olivares Connect in order to contact the contributor
Submitted on : Friday, December 2, 2016 - 11:51:37 AM
Last modification on : Saturday, June 25, 2022 - 7:43:22 PM
Long-term archiving on: : Tuesday, March 21, 2017 - 4:08:33 AM


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Yahya Benkaouz, Mohammed Erradi, Anne-Marie Kermarrec. Nearest Neighbors Graph Construction: Peer Sampling to the Rescue. 4th International Conference, NETYS 2016, Marrakech, Morocco, May 18-20, 2016, Revised Selected Papers, May 2016, Marrakech, Morocco. pp.48 - 62, ⟨10.1007/978-3-319-46140-3_4⟩. ⟨hal-01407514⟩



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