Robust and Reliable Feature Extractor Training by Using Unsupervised Pre-training with Self-Organization Map

Recent research has shown that deep neural network is very powerful for object recognition task. However, training the deep neural network with more than two hidden layers is not easy even now because of regularization problem. To overcome such a regularization problem, some techniques like dropout and de-noising were developed. The philosophy behind de-noising is to extract more robust features from the training data. For that purpose, randomly corrupted input data are used for training an auto-encoder or Restricted Boltzmann machine (RBM). In this paper, we propose unsupervised pre-training with a Self-Organization Map (SOM) to increase robustness and reliability of feature extraction. The basic idea is that instead of random corruption, our proposed algorithm works as a feature extractor so that corrupted input maintains the main skeleton or structure of original data. As a result, our proposed algorithm can extract more robust features related to input data.