Fuzzy Removing Redundancy Restricted Boltzmann Machine: Improving Learning Speed and Classification Accuracy

摘要

To improve the feature extraction ability and shorten the learning time, fuzzy removing redundancy restricted Boltzmann machine (F3RBM) is developed. The features extracted by F3RBM with unsupervised learning are imported into support vector machine (SVM) to establish F3RBM-SVM model, which achieves fast and high-precision automatic classification of different samples. To expand the feature extraction capability of restricted Boltzmann machine (RBM), the deterministic parameters of control model are replaced by fuzzy numbers in view of the superiority of fuzzy idea and the redundancy removal mechanism is introduced. Comparing the feature similarity of hidden units with the threshold value, if the similarity is greater than the threshold value, they are considered to be redundant units with the same features. The redundant units are removed to achieve further dimension reduction. Finally, the learning speed, feature extraction ability, and classification accuracy of different models are compared in MINIST handwritten dataset, Fashion MNIST dataset, and Olivetti Face dataset. The experimental results show that the feature extraction capability of FRBM and F3RBM is better than that of RBM. When there are a large number of hidden units, the learning speed of F3RBM is obviously faster than that of FRBM. The features extracted from F3RBM are imported into the SVM to build F3RBM-SVM model, which improves the classification accuracy and learning speed than general classifier. When adding other noises, F3RBM-SVM has better robustness than other models.