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首页> 外文期刊>IEEE Geoscience and Remote Sensing Letters >Multiview Active Learning Optimization Based on Genetic Algorithm and Gaussian Mixture Models for Hyperspectral Data
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Multiview Active Learning Optimization Based on Genetic Algorithm and Gaussian Mixture Models for Hyperspectral Data

机译：基于遗传算法和高斯混合模型进行高光谱数据的多视图主动学习优化

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摘要

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.
机译：在这封信中，我们提出了一种基于遗传算法（GA）和高斯混合模型（GMMS）的新颖最佳观看生成框架，以改善多视图主动学习（MV-A1）。通过迭代选择最具信息性样本来扩大训练数据集，以提高分类性能。通过使用多个视图来构建多个分类器，可以更准确地估计每个未标记的样本的信息内容。 MV-Al方法更固有地适用于诸如Hyperspectral图像的高维数据。这种混合框架同时构建了多样化的多数和景色。所提出的算法有两个主要步骤。在第一步中，通过应用基于群集失真函数的GMM，确定可用的独立视图的实际数量。在下一步中，混合GA方法使用两个不同的标准选择最佳的视图组合。该实验是在两个基准高光谱数据集中进行的，即肯尼迪航天中心（KSC）和印度松树。结果表明，与传统观点生成方法相比，观点的多样性和充分性增加。此外，MV-A1的性能也得到了显着改善。

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