A Study Of Several Highly Complex Problems In Pattern Recognition Based On Modular Neural Networks And Manifold Learning Technique

At present, there have been many classification techniques well developed in pattern recognition field. However, the broadening of fields and the enlarging of sizes of datasets dealt with in real applications are challenging the learning speeds and classification accuracies of all sorts of pattern classification systems. Specially, some special complex cases such as the classification problems with large size of training set, unbalanced training set and partially labeled training set demand designing some special and more effective classification models. Therefore, it is necessary for us to thoroughly investigate the classification models so as to solve those highly complex problems. This thesis is focused on comprehensively and systemically solving the classification problems with large size of training set and imbalanced training set by using modular neural networks, as well as semi-supervised classification problems by using manifold learning. The obtained results enrich and perfect the classification models and enhance the classification performance for these complex problems. The main works in the thesis can be stated as follows:1. A classification structure of matrix modular neural network based on a novel task decomposition technique was proposed, which can decompose a complex task into several easier subtasks between subspace pairs. Each subtask is then solved by a simple perceptron. All of these perceptron modules form a perceptron matrix structure, which produces a matrix of outputs that will be fed to an integration machine so that a classification decision can be efficiently made. This method can greatly speed up training of neural networks and obviously enhance the generalization capability for distinguishing unknown samples according to our experiments and theoretical analyses.2. The proposed matrix modular neural network was successfully applied into face recognition and palmprint recognition. Furthermore, for palmprint recognition, a feature extraction technique called '2DPCA(w/o3)+PCA', which consumes less eatraction time but obtains better classification performance than other feature extraction techniques, was also proposed.3. A structure of matrix modular neural network was proposed to deal with the imbalanced pattern classification problems. By this matrix modular neural network, an imbalanced classification problem can be transformed into a set of symmetrical two-class problems, each of which can be solved easily by a simple network. The experimental results showed that the matrix modular neural network could reduce the CPU consumption for the training, and also improve the classification performance.4. A modified version to semi-supervised learning algorithm based on Riemannian manifold and mapping for minimum error sum was proposed to make it applicable to multi-classes semi-supervised learning. The modified algorithm largely increases the learning speed, and at the same time attains the satisfying classification performance, which is not lower than that of the original algorithm.5. An improved version to spectral mapping, referred to as semi-supervised spectral mapping, was proposed to implement semi-supervised learning. This new method adds the label information into the mapping process, and adopts the geodesic distance rather than Euclidean distance as the measure of the difference between two data points. The experimental results show that the proposed method yields significant benefits for partially labeled classification with respect to the previous methods.