In many modern computer application problems, the classification of image data plays an important role. Among many different supervised machine learning models, convolutional neural networks (CNNs) and linear discriminant analysis (LDA) as well as sophisticated variants thereof are popular techniques. In this work, two different domain decomposed CNN models are experimentally compared for different image classification problems. Both models are loosely inspired by domain decomposition methods and in addition, combined with a transfer learning strategy. The resulting models show improved classification accuracies compared to the corresponding, composed global CNN model without transfer learning and besides, also help to speed up the training process. Moreover, a novel decomposed LDA strategy is proposed which also relies on a localization approach and which is combined with a small neural network model. In comparison with a global LDA applied to the entire input data, the presented decomposed LDA approach shows increased classification accuracies for the considered test problems.

翻译：在许多现代计算机应用问题中，图像数据的分类起着重要作用。在众多不同的监督机器学习模型中，卷积神经网络（CNN）和线性判别分析（LDA）及其复杂变体是流行的技术。本文针对不同的图像分类问题，通过实验比较了两种不同的域分解CNN模型。这两种模型都受到了域分解方法的启发，并额外结合了迁移学习策略。与未使用迁移学习的相应全局组合CNN模型相比，所得模型显示出更高的分类准确率，同时也有助于加速训练过程。此外，本文提出了一种新颖的分解LDA策略，该策略同样基于局部化方法，并与一个小型神经网络模型相结合。与应用于整个输入数据的全局LDA相比，所提出的分解LDA方法在所考虑的测试问题上表现出更高的分类准确率。