Nearest Neighbors Graph Construction: Peer Sampling to the Rescue - Inria - Institut national de recherche en sciences et technologies du numérique Access content directly
Conference Papers Year : 2016

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.
Fichier principal
Vignette du fichier
paper_13.pdf (1004.88 Ko) Télécharger le fichier
Origin : Files produced by the author(s)
Loading...

Dates and versions

hal-01407514 , version 1 (02-12-2016)

Identifiers

Cite

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⟩
519 View
1145 Download

Altmetric

Share

Gmail Facebook X LinkedIn More