Generative Correlation Discovery Network for Multilabel Learning The goal of Multilabel learning is to predict multiple labels of each single instance. This is a challenging problem since the training data is limited, longtail label distribution, and complicated label correlations. Generally, more training samples and label correlation knowledge would benefit the learning performance. However, it is difficult to obtain largescale welllabeled datasets, and building such a label correlation map requires sophisticated semantic knowledge. To this end, we propose an endtoend Generative Correlation Discovery Network (GCDN) method for multilabel learning in this paper. GCDN captures the existing data distribution, and synthesizes diverse data to enlarge the diversity of the training features; meanwhile, it also learns the label correlations based on a specificallydesigned, simple but effective correlation discovery network to automatically discover the label correlations and considerately improve the label prediction accuracy. Extensive experiments on several benchmarks are provided to demonstrate the effectiveness, efficiency, and high accuracy of our approach.

Improved Multiview Multilabel Learning with Incomplete Views and Labels Multiview multilabel learning has attracted the attention of many scholars and widely used in multiple fields. While in realworld applications, due to the lack of manpower and equipment failure, the data sets to be processed maybe loss some labels or views. Moreover, most multiview multilabel 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 multiview multilabel learning with incomplete views and labels (IMVLIV). In framework of IMVLIV, the usage of labelspecific 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 lowrank assumption matrix restores missing views; global and local label correlations are taken into consideration with clustering technology; a consensus multiview 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 multiview multilabel learning method with incomplete views and labels by the learning of labelspecific features, label correlation matrix, lowrank assumption matrix, global and local label correlations, and complementary information. Experimental results validate that IMVLIV achieves a better performance and it is superior to the classical multiview learning methods and multilabel learning methods.