Discriminatively Relabel for Partial Multilabel Learning Partial multilabel learning (PML) deals with the problem where each training example is assigned multiple candidate labels, only a part of which are correct. To learn from such PML examples, the straightforward model training tends to be misled by the noise candidate label set. To alleviate this problem, a coupled framework is established in this paper to learn the desired model and perform the relabeling procedure alternatively. In the relabeling procedure, instead of simply extracting relative label confidences, or deterministically eliminating low confidence labels and preserving high confidence labels as groundtruth ones, we introduce a soft sign thresholding operator to adaptively strengthen candidate labels with high confidence and weaken candidate labels with low confidence, which enlarges the difference of confidences of candidate labels within allowable range. We further show that the resulting nonconvex quadratic programming (QP) optimization problem can be relaxed into a convex QP problem with proper conditions. Extensive experiments on synthesized and realworld data sets demonstrate the effectiveness of our proposed 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.