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Multiview Active Learning Optimization Based on Genetic Algorithm and Gaussian Mixture Models for Hyperspectral Data.

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Jamshidpour, Nasehe; Safari, Abdolreza; Homayouni, Saeid (9999). Multiview Active Learning Optimization Based on Genetic Algorithm and Gaussian Mixture Models for Hyperspectral Data. IEEE Geoscience and Remote Sensing Letters . DOI: 10.1109/LGRS.2019.2914858. (Sous Presse)

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Résumé

In this letter, we proposed a novel optimal view generation framework based on the genetic algorithm (GA) and Gaussian mixture models (GMMs) to improve multiview active learning (MV-AL). AL methods enlarge training data sets, by iteratively selecting the most informative samples, in order to improve the classification performance. By using multiple views to build multiple classifiers, the information content of each unlabeled samples can be more accurately estimated. The MV-AL methods are more inherently suitable for high-dimensional data such as hyperspectral images. This hybrid framework simultaneously constructs the optimal number of diverse and sufficient views. The proposed algorithm has two main steps. In the first step, by applying a cluster distortion function-based GMMs, the actual number of available independent views is determined. In the next step, a hybrid GA approach selects the optimal combination of views using two different criteria. The experiments were conducted on two benchmark hyperspectral data sets, namely, Kennedy Space Center (KSC) and Indian Pines AVIRIS. The results demonstrated an increase in diversity and sufficiency of the views compared to the traditional view generation methods. Furthermore, the performance of MV-AL has also been significantly improved.

Type de document: Article
Mots-clés libres: active learning (AL); Gaussian mixture models (GMMs); genetic algorithms (GAs); multiview (MV) learning; view generation methods
Centre: Centre Eau Terre Environnement
Date de dépôt: 29 nov. 2019 14:27
Dernière modification: 29 nov. 2019 14:27
URI: http://espace.inrs.ca/id/eprint/9552

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