Improved Multi-view Multi-label Learning with Incomplete Views and Labels

Multi-view multi-label learning has attracted the attention of many scholars and widely used in multiple fields. While in real-world applications, due to the lack of manpower and equipment failure, the data sets to be processed maybe loss some labels or views. Moreover, most multi-view multi-label learning methods neglect the global and local label correlations of both the whole data set and each view and the complementary information coming from different views sometimes. Furthermore, some methods ignore such a phenomenon that each class label might be determined by some specific features of its own. In order to improve the performance of such methods, in this paper, we develop an improved multi-view multi-label learning with incomplete views and labels (IMVL-IV). In framework of IMVL-IV, the usage of label-specific features makes the decision of label be determined by some specific features rather than all features so that we can pay more attention to portion specific features and save time; the introduction of label correlation matrix offsets the defect of missing labels; the adoption of low-rank assumption matrix restores missing views; global and local label correlations are taken into consideration with clustering technology; a consensus multi-view representation is put to use to encode the complementary information from different views. Different from traditional learning methods, this is the first attempt to design a multi-view multi-label learning method with incomplete views and labels by the learning of label-specific features, label correlation matrix, low-rank assumption matrix, global and local label correlations, and complementary information. Experimental results validate that IMVL-IV achieves a better performance and it is superior to the classical multi-view learning methods and multi-label learning methods.