A Genetic Programming Approach for Searching on Nearest Neighbors Graphs

Muñoz, Javier Alvaro Vargas; Dias, Zanoni; Torres, Ricardo da Silva

doi:10.1145/3323873.3325014

Cited by 3 publications

(2 citation statements)

References 26 publications

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“…Those functions are expected to lead to more efficient searches. -We validated our search approach on a recent technique for NN graph construction [51], along with the three other previous techniques considered [50], showing the consistency of our results independently of the dataset and the NN graph construction technique. Also, parts of this work were presented in the PhD thesis of the first author [49].…”

Section: Contributionsmentioning

confidence: 58%

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A genetic programming approach for searching on nearest neighbors graphs

Muñoz

Dias

Torres

2022

Multimed Tools Appl

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Nearest neighbors graphs have gained a lot of attention from the information retrieval community since they were demonstrated to outperform classical approaches in the task of approximate nearest neighbor search. These approaches, firstly, index feature vectors by using a graph-based data structure. Then, for a given query, the search is performed by traversing the graph in a greedy-way, moving in each step towards the neighbor of the current vertex that is closer to the query (based on a distance function). However, local topological properties of vertices could be also considered at the moment of deciding the next vertex to be explored. In this work, we introduce a Genetic Programming framework that combines topological properties along with the distance to the query, aiming to improve the selection of the next vertex in each step of graph traversal and, therefore, reduce the number of vertices explored (scan rate) to find the true nearest neighbors. Experimental results, conducted over three large collections of feature vectors and four different graph-based techniques, show significant gains of the proposed approach over classic graph-based search algorithms.

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Section: Contributionsmentioning

confidence: 58%

“…This paper is an extension of our previous work [50]. Different from that paper, we included two new topological properties to the GP framework that improved our previous results.…”

Section: Contributionsmentioning

confidence: 85%

A genetic programming approach for searching on nearest neighbors graphs

Muñoz

Dias

Torres

2022

Multimed Tools Appl

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Two-stage routing with optimized guided search and greedy algorithm on proximity graph

Wang

et al. 2021

Knowledge-Based Systems

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A comprehensive survey and experimental comparison of graph-based approximate nearest neighbor search

Wang

Yue

et al. 2021

Proc. VLDB Endow.

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Approximate nearest neighbor search (ANNS) constitutes an important operation in a multitude of applications, including recommendation systems, information retrieval, and pattern recognition. In the past decade, graph-based ANNS algorithms have been the leading paradigm in this domain, with dozens of graph-based ANNS algorithms proposed. Such algorithms aim to provide effective, efficient solutions for retrieving the nearest neighbors for a given query. Nevertheless, these efforts focus on developing and optimizing algorithms with different approaches, so there is a real need for a comprehensive survey about the approaches' relative performance, strengths, and pitfalls. Thus here we provide a thorough comparative analysis and experimental evaluation of 13 representative graph-based ANNS algorithms via a new taxonomy and fine-grained pipeline. We compared each algorithm in a uniform test environment on eight real-world datasets and 12 synthetic datasets with varying sizes and characteristics. Our study yields novel discoveries, offerings several useful principles to improve algorithms, thus designing an optimized method that outperforms the state-of-the-art algorithms. This effort also helped us pinpoint algorithms' working portions, along with rule-of-thumb recommendations about promising research directions and suitable algorithms for practitioners in different fields.

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A Genetic Programming Approach for Searching on Nearest Neighbors Graphs

Cited by 3 publications

References 26 publications

A genetic programming approach for searching on nearest neighbors graphs

A genetic programming approach for searching on nearest neighbors graphs

Two-stage routing with optimized guided search and greedy algorithm on proximity graph

A comprehensive survey and experimental comparison of graph-based approximate nearest neighbor search

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