| Abstract: | Graph carries out a key role in graph-based semi-supervised label propagation, as it clarifies the structure of the data manifold. The performance of label propagation methods depends on the adopted graph and can be enhanced by merging different graphs that are obtained from multiple sources of information. While there exist algorithms that perform graph fusion they have several weaknesses. Most of these algorithms define graph fusion and label propagation as two separate tasks. Moreover, when the number of data expands, these strategies are not well-suited due to the use of transductive learning in the label propagation phase which makes the label prediction for unseen samples difficult. Furthermore, very few algorithms extract the information contained in the label space. Additionally, most of the graph fusion techniques adopt equal or static weights for different views, which is not the best choice as distinctive features (hence different graphs) contain various information. To overcome these shortcomings, we propose an Auto-weighted Multi-view Semi-Supervised Learning method (AMSSL), which is based on an inductive learning algorithm (i.e., Flexible Manifold Embedding) and profited a projection matrix for predicting the labels of out-of-sample data. The proposed AMSSL method represents a unified framework that dynamically fuses various information obtained from different features and also from the label space and adaptively designates appropriate weights according to the usefulness of each view. Our experimental results on seven small and large image datasets demonstrate the superiority of the proposed method compared to the use of one single feature and other state-of-the-art graph fusion methods. |
