Research On Multi-view Learning Under Complex Application Situations

Multi-view learning focus on the learning tasks whose data have naturally multiple featurerepresentations. In the past decade, multi-view learning has been studied widely and gained manytheoretical results and practical algorithms. However, so far, most of the works focus on themulti-view classification, clustering and dimension reduction tasks and are only applicable underthe full-paired circumstance. In contrast, real world applications constantly bring out new learningsettings and require new solutions. For example, multi-view classification and retrieval sometimeslargely depends on suitable metrics, which needs multi-view metric learning model. Anotherinteresting application of multi-view learning is to transplant the learning idea into single-viewlearning. This dissertation proposes to construct a new view to reveal the data cluster structure anduses it to learn classification and clustering simultaneously. Furthermore under some rigidenviroments, the collected multi-view data may not fully paired due to data transmition, maliciousattack, or simply application restriction. Thus how to design proper algorithms to meet the newchallanges is the main concern of this dissertation. The main contributions of this dissertation aresummarized below,1) Develop a co-training style multi-view metric learning algorithm called co-metric. Followingthe co-training’s learning scheme, co-metric learns metrics for each data view and makes themto teach each other to boost their performances under the semi-supervised circumstance. Thekey step in co-metric algorithm is choosing the confidently-labeled samples in each view withtheir learned metric. To achieve this goal, we design a simple but effective method by settingthe parameter K of K-nearest neighbor classification to a large integer. The algorithm uses theexisting single-view metric learning algorithm and is very easy to implement. Experimentsdemostrate its effectiveness.2) Propose a cross-view metric learning model under the totally-unpaired settings. The model,called MLHD, first maps the samples in each view into a common space, then aligns theirpriors p(sample)s and posterior p(label|sample) at the same time. Further, it can bereparameterized only with a positive semi-definite matrix. And by introducing a LogDetfunction to regularize this matrix parameter, the model can be easily optimized with Bregmanprojection algorithm which automatically maintains the positive semi-definite property of thematrix.. Also, it is proved that this model has an equivalent optimization problem which onlydepends on the inner product of the samples. As a result, the model can be convenientlykernelized. Experiments on the cross-language retrieval and cross-domain object recognition task show its effectiveness.3) Introduce a new side information, cross-view must-link and cross-view cannot-link, and applythem in the multi-view classification under totally-unpaired settings. The new sideinformation is a natural extention of widely-used single-view must-link and single-viewcannot-link, and indicates whether the two samples in different views have the same label ornot. To apply this information into multi-view classification with totally-unpaired data, wemodify the classical regularization model and add new side information related regularizationterm. Experiments demostrate the effectiveness of this side information.4) Propose a simultaneously learning classification and clustering model for single-view datathrough constructing a new cluster structure view. The proposed model uses the clusterstructure view to connect the classification and clustering tasks for a single view data and canbe optimized by block gradient descendent algorithm. Comparing with the previous methodproposed by Cai et al., the model is more flexible and can be easily generalized tosemi-supervised circumstance by manifold regularization. Also, this algorithm is much faster.