Transductive Multilabel Learning via Label Set Propagation

Transductive Multi-Label Learning via Alpha Matting

Multi-label learning deals with the problems when each instance can be assigned to multiple classes simultaneously, which are ubiquitous in real-world learning tasks. In this paper, we propose a new multilabel learning method, which is able to exploit unlabeled data to obtain an effective model for assigning appropriate multiple labels to instances. The proposed method is called T (TRansduct...

Stacking Label Features for Learning Multilabel Rules

Dependencies between the labels is commonly regarded as the crucial issue in multilabel classification. Rules provide a natural way for symbolically describing such relationships, for instance, rules with label tests in the body allow for representing directed dependencies like implications, subsumptions, or exclusions. Moreover, rules naturally allow to jointly capture both local and global la...

Transductive Multi-label Zero-shot Learning

Zero-shot learning has received increasing interest as a means to alleviate the often prohibitive expense of annotating training data for large scale recognition problems. These methods have achieved great success via learning intermediate semantic representations in the form of attributes and more recently, semantic word vectors. However, they have thus far been constrained to the single-label...

Transductive Learning via Model Selection

A novel transductive learning algorithm is proposed, which is based on the use of model selection. In its simplest form there are k possible labels, m labeled points and one unlabeled point. One model is built for each possible classification of the unlabeled point yM+1 = Li, i = 1, ..., k, using all M + 1 points and M + 1 labels. Any standard model selection criterion can then be applied to se...

Multiclass-Multilabel Learning when the Label Set Grows with the Number of Examples