Deep Self-Organizing Maps for Unsupervised Image Classification
| Autor: | Kasun Amarasinghe, Chathurika S. Wickramasinghe, Milos Manic |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
Self-organizing map
Contextual image classification business.industry Computer science Deep learning 020208 electrical & electronic engineering Pattern recognition 02 engineering and technology Autoencoder Computer Science Applications Control and Systems Engineering Feature (computer vision) 0202 electrical engineering electronic engineering information engineering Benchmark (computing) Unsupervised learning Noise (video) Artificial intelligence Electrical and Electronic Engineering business Information Systems |
| Zdroj: | IEEE Transactions on Industrial Informatics. 15:5837-5845 |
| ISSN: | 1941-0050 1551-3203 |
| Popis: | The deep self-organizing map (DSOM) was introduced to embed hierarchical feature abstraction capability to self-organizing maps (SOMs). This paper presents an extended version of the original DSOM algorithm (E-DSOM). E-DSOM enhances the DSOM in two ways—learning algorithm is modified to be completely unsupervised, and architecture is modified to learn features of different resolution in hidden layers. E-DSOM has three main advantages over the original DSOM: 1) improved classification accuracy; 2) improved generalization capability; and 3) need of fewer sequential layers (reduced training time). E-DSOM was tested on benchmark and real-world datasets and was compared against DSOM, SOM, sStacked autoencoder (AE), and stacked convolutional autoencoder (CAE). Experimental results showed that the E-DSOM outperformed DSOM with improvements of classification accuracy up to 15% while saving training time up to 19% on all datasets. Moreover, E-DSOM evidenced better generalization capability compared to the DSOM by showing superior performance on all datasets with induced noise. Further, E-DSOM showed comparable performance to the AE and the CAE while outperforming them on two datasets. |
| Databáze: | OpenAIRE |
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