On View Construction Of Multi-view Learning: Single Tasks And Multiple Tasks

According to different obtained ways, the pattern can be composed of the corresponding different attribute sets, where each attribute set can be taken as one view. In specific application areas, from different visual views we can obtain different visual effects for a given pattern, which can form different directions or more multiple-level information to gain better performance. The information which comes from different perspectives of data may be complemental. If making full use of them, more informative solutions are achieved from the different angles of problem.The idea is applied vividly and incisively in semi-supervised learning which is an algorthim between supervised learning and unsupervised learning. In semi-supervised learning, there is a large pool of unlabeled data and comparatively few labeled data. Unlabeled data are taken advantage to assist the labeled data to improve the performance of learner. For example, co-training which a simple and effective method for semi-supervised learning is works under a two-view setting. In co-training, initially two separate classifiers are trained from each view of labeled data, then the most confident ones will be respectively selected to add to the sets of initial labeled examples. Finally, the labeled data in the training set are increased. So far, many other novel multiple-view semi-supervised learning algorithms are raised based on the setting of co-training. The successful application of those methods demonstrates that the feasibility of multiple-view learning is obvious. However, it is limited by the fact that the data set in real-world application only has one view. Therefore, it is meaningful that an effective method which can generate multiple views from the original data is proposed.Firstly, we propose a feature selection approach to artificially generate multiple views by means of genetic algorithm in the thesis. The feature subsets which have the characteristic to distinguish the target of designed model are selected to serve as multiple views. The effectiveness of this novel approach and the result which is gained by comparing with a random feature split method are evaluated on several different classification problems.Secondly, in order to gain better generalization, the multiple task learning is used to take the place of the traditional learning method. In this thesis, the application in the traffic flow forecasting and the face recognition illustrate that the multiple task learning has the ability to improve the generalization.Finally, we also combine the proposed method with multi-task learning. In the multi-task learning, the domain specific information which is included in the training signals of other tasks is used to improve generalization. In fact, the training signals for the extra tasks serve as an inductive bias. The sufficiency for multi-view multi-task learning method is also shown in several different classification problems where encouraging experimental results are observed.