A challenging problem of multi-label learning is that both the label space and the model complexity will grow rapidly with the increase in the number of labels, and thus makes the available training samples insufficient for training a proper model. In this paper, we eliminate this problem by learning a mapping of each label in the feature space as a robust subspace, and formulating the predicti...

A plane, passing through the center and orthogonal to a diagonal, slices cube into two identical halves each having three triangles, pentagons hexagon. If you label ten vertices of half-cube with numbers, then face is said receive an induced given by sum all around it. Label digits 0 9 so that labels triangles constitute six consecutive multiples fixed number, or more generally numbers in arith...

Multi-label classification is the problem that classes are not mutually exclusive, so that an example may belong to more than one category. This poses challenges to the traditional pattern recognition theory where class overlap means classification error. Multi-label classification arises typically in semantic scene classification, text categorization, medical diagnosis, and bioinformatics. How...

Label propagation is a popular graph-based semisupervised learning framework. So as to obtain the optimal labeling scores, the label propagation algorithm requires an inverse matrix which incurs the high computational cost ofO(n+cn), where n and c are the numbers of data points and labels, respectively. This paper proposes an efficient label propagation algorithm that guarantees exactly the sam...

For multi-label image classification, we use active learning to select examplelabel pairs to acquire labels from experts. The core of active learning is to select the most informative examples to request their labels. Most previous studies in active learning for multi-label classification have two shortcomings. One is that they didn't pay enough attention on label correlations. The other shortc...

Multi-label learning originated from the investigation of text categorization problem, where each document may belong to several predefined topics simultaneously. In multi-label learning, the training set is composed of instances each associated with a set of labels, and the task is to predict the label sets of unseen instances through analyzing training instances with known label sets. In this...

We design a new label shortest path algorithm by applying the concept of a pseudo permanent label. This approach allows an algorithm to partition the set of nodes into two new sets: pseudo permanently labeled nodes and its complementary set. From this point of view, this new label method can be considered as label setting and is also a Dijkstra (1959) method. Moreover, during the execution of e...

Multi-label classification is an important machine learning task wherein one assigns a subset of candidate labels to an object. In this paper, we propose a new multi-label classification method based on Conditional Bernoulli Mixtures. Our proposed method has several attractive properties: it captures label dependencies; it reduces the multi-label problem to several standard binary and multi-cla...

Label propagation algorithm (LPA) is an extremely fast community detection method and is widely used in large scale networks. In spite of the advantages of LPA, the issue of its poor stability has not yet been well addressed. We propose a novel node influence based label propagation algorithm for community detection (NIBLPA), which improves the performance of LPA by improving the node orders of...