A new parallel training algorithm for optimum-path forest-based learning
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Data
2017-01-01
Autores
Culquicondor, Aldo
Castelo-Fernández, César
Papa, João Paulo [UNESP]
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Resumo
In this work, we present a new parallel-driven approach to speed up Optimum-Path Forest (OPF) training phase. In addition, we show how to make OPF up to five times faster for training using a simple parallel-friendly data structure, which can achieve the same accuracy results to the ones obtained by traditional OPF. To the best of our knowledge, we have not observed any work that attempted at parallelizing OPF to date, which turns out to be the main contribution of this paper. The experiments are carried out in four public datasets, showing the proposed approach maintains the trade-off between efficiency and effectiveness.
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Graph algorithms, Optimum-path forest, Parallel algorithms
Como citar
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), v. 10125 LNCS, p. 192-199.