Domain-decomposed image classification algorithms using linear discriminant analysis and convolutional neural networks
Fuente:
arXiv
Gespeichert in:
| Hauptverfasser: | , , |
|---|---|
| Format: | Preprint |
| Veröffentlicht: |
2024
|
| Schlagworte: | |
| Online-Zugang: | |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| _version_ | 1866916461805568000 |
|---|---|
| author | Klawonn, Axel Lanser, Martin Weber, Janine |
| author_facet | Klawonn, Axel Lanser, Martin Weber, Janine |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_23359 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Domain-decomposed image classification algorithms using linear discriminant analysis and convolutional neural networks Klawonn, Axel Lanser, Martin Weber, Janine Computer Vision and Pattern Recognition Machine Learning Numerical Analysis 68T07, 68W10, 68W15, 65N55 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. |
| title | Domain-decomposed image classification algorithms using linear discriminant analysis and convolutional neural networks |
| topic | Computer Vision and Pattern Recognition Machine Learning Numerical Analysis 68T07, 68W10, 68W15, 65N55 |
| url | https://arxiv.org/abs/2410.23359 |