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.

Consistency Meets Inconsistency: A Unified Graph Learning Framework for Multiview Clustering Graph Learning has emerged as a promising technique for multiview clustering, and has recently attracted lots of attention due to its capability of adaptively learning a unified and probably better graph from multiple views. However, the existing multiview graph learning methods mostly focus on the multiview consistency, but neglect the potential multiview inconsistency (which may be incurred by noise, corruptions, or viewspecific characteristics). To address this, this paper presents a new graph learningbased multiview clustering approach, which for the first time, to our knowledge, simultaneously and explicitly formulates the multiview consistency and the multiview inconsistency in a unified optimization model. To solve this model, a new alternating optimization scheme is designed, where the consistent and inconsistent parts of each singleview graph as well as the unified graph that fuses the consistent parts of all views can be iteratively learned. It is noteworthy that our multiview graph learning model is applicable to both similarity graphs and dissimilarity graphs, leading to two graph fusionbased variants, namely, distance (dissimilarity) graph fusion and similarity graph fusion. Experiments on various multiview datasets demonstrate the superiority of our approach. The MATLAB source code is available at https:github.comyouweiliangConsistentGraphLearning.