A New Parallel Training Algorithm for Optimum-Path Forest-Based Learning
نویسندگان
چکیده
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.
منابع مشابه
Near-Minimum-Time Motion Planning of Manipulators along Specified Path
The large amount of computation necessary for obtaining time optimal solution for moving a manipulator on specified path has made it impossible to introduce an on line time optimal control algorithm. Most of this computational burden is due to calculation of switching points. In this paper a learning algorithm is proposed for finding the switching points. The method, which can be used for both ...
متن کاملOptimum Path Forest Approach for Image Retrieval based on Context
CBIR System consist of large datasets with millions of image samples for statistical analysis, hence putting tremendous challenge for pattern recognition techniques, which needs to be more efficient without compromising effectiveness. The image samples are stored in a database in the form of feature vectors. Pattern Recognition Technique requires a high computational burden for learning the dis...
متن کاملImproving Accuracy and Speed of Optimum-Path Forest Classifier Using Combination of Disjoint Training Subsets
The Optimum-Path Forest (OPF) classifier is a recent and promising method for pattern recognition, with a fast training algorithm and good accuracy results. Therefore, the investigation of a combining method for this kind of classifier can be important for many applications. In this paper we report a fast method to combine OPF-based classifiers trained with disjoint training subsets. Given a fi...
متن کاملA path- and label-cost propagation approach to speedup the training of the optimum-path forest classifier
19 In general, pattern recognition techniques require a high computational burden for learning the discriminating functions that are responsible to separate samples from distinct classes. As such, there are several studies that make effort to employ machine learning algorithms in the context of “big data” classification problems. The research on this area ranges from Graphics Processing Units-b...
متن کاملLearning to Identify Non-Technical Losses with Optimum-Path Forest
In this work we have proposed an innovative and accurate solution for non-technical losses identification using the Optimum-Path Forest (OPF) classifier and its learning algorithm. Results in two datasets demonstrated that OPF outperformed the state of the art pattern recognition techniques and OPF with learning achieved better results for automatic nontechnical losses identification than recen...
متن کامل