Feature Extraction-Based Deep Self-Organizing Map

We propose in this work, a new approach for feature extraction based on deep Self-Organizing Map (SOM) network, named Generalized Unsupervised Deep SOM (G-UDSOM). This work presents an enhancement of the classic unsupervised deep SOM (UDSOM) algorithm in two ways. First, we modify the UDSOM Sub-sampling module in such a way that the image reconstruction phase is applied only in the feature construction phase. Second, the modified sub-sampling module learn feature of different sizes and resolutions through different map and patch sizes, which allows building parallel learning architecture. We add compact reconstruction constraint through the SOM learning based on convolutional auto-encoder. Thus. G-UDSOM allows extracting latent representation through its internal layer, by minimizing cost function. This function is composed of two terms: the cost SOM function mean square error of reconstruction. The second objective of our extended version is to achieve reduced computation time while improving accuracy and generalization capability. Different from the earlier proposed UDSOM architectures, G-UDSOM uses parallel SOMs without any reconstruction in the hidden layers. We evaluate our proposed approach on MNIST and STL-10 datasets. Experiment results show that our proposed approach outperforms deep SOMs models in terms of classification accuracy, training time and computational complexity. Our method is also can be generalized across different dataset without any pre-trained models.