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Open Access

Ranking with Adaptive Neighbors

Cixi Hanvos Yucai High School, Ningbo 315300, China.
School of Computing, Informatics, Decision Systems Engineering, Arizona State University, Tempe, AZ 85281, US.
School of Computer Science and Center for OPTical IMagery Analysis and Learning (OPTIMAL), Northwestern Polytechnical University, Xi’an 710072, China.
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Abstract

Retrieving the most similar objects in a large-scale database for a given query is a fundamental building block in many application domains, ranging from web searches, visual, cross media, to document retrievals. State-of-the-art approaches have mainly focused on capturing the underlying geometry of the data manifolds. Graph-based approaches, in particular, define various diffusion processes on weighted data graphs. Despite success, these approaches rely on fixed-weight graphs, making ranking sensitive to the input affinity matrix. In this study, we propose a new ranking algorithm that simultaneously learns the data affinity matrix and the ranking scores. The proposed optimization formulation assigns adaptive neighbors to each point in the data based on the local connectivity, and the smoothness constraint assigns similar ranking scores to similar data points. We develop a novel and efficient algorithm to solve the optimization problem. Evaluations using synthetic and real datasets suggest that the proposed algorithm can outperform the existing methods.

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Tsinghua Science and Technology
Pages 733-738
Cite this article:
Li M, Li L, Nie F. Ranking with Adaptive Neighbors. Tsinghua Science and Technology, 2017, 22(6): 733-738. https://doi.org/10.23919/TST.2017.8195354

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Received: 25 June 2017
Revised: 08 August 2017
Accepted: 24 August 2017
Published: 14 December 2017
© The author(s) 2017
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