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Deep Self-Organizing Maps for Unsupervised Image Classification
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DOI：10.1109/TII.2019.2906083.png)
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Abstract
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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.
Keywords:
Self-organizing feature maps
Training
Computer architecture
Neurons
Unsupervised learning
Deep learning
Data mining
Deep learning
deep self-organizing maps (DSOMs)
generalization
image classification
unsupervised learning
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