A genetic programming approach for searching on nearest neighbors graphs
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- @Article{Munoz:2022:MTA,
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author = "Javier A. Vargas Munoz and Zanoni Dias and
Ricardo {da Silva Torres}",
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title = "A genetic programming approach for searching on
nearest neighbors graphs",
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journal = "Multimedia Tools and Applications",
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year = "2022",
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volume = "81",
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pages = "23449--23472",
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month = jul,
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keywords = "genetic algorithms, genetic programming, Distance
learning, Genetic programming, Image retrieval,
Graph-based indexes, Nearest neighbour searches,
Network theory",
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ISSN = "1380-7501",
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DOI = "doi:10.1007/s11042-022-12248-w",
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abstract = "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.",
- }
Genetic Programming entries for
Javier Alvaro Vargas Munoz
Zanoni Dias
Ricardo da Silva Torres
Citations