text: Multi-label learning deals with the classification problems where each instance can be assigned with multiple labels simultaneously. Conventional multi-label learning approaches mainly focus on exploiting label correlations. It is usually assumed, explicitly or implicitly, that the label sets for training instances are fully labeled without any missing labels. However, in many real-world multi-label datasets, the label assignments for training instances can be incomplete. Some ground-truth labels can be missed by the labeler from the label set. This problem is especially typical when the number instances is very large, and the labeling cost is very high, which makes it almost impossible to get a fully labeled training set. In this paper, we study the problem of large-scale multi-label learning with incomplete label assignments. We propose an approach, called MPU, based upon positive and unlabeled stochastic gradient descent and stacked models. Unlike prior works, our method can effectively and efficiently consider missing labels and label correlations simultaneously, and is very scalable, that has linear time complexities over the size of the data. Extensive experiments on two real-world multi-label datasets show that our MPU model consistently outperform other commonly-used baselines.
  @cite_19: 
  @cite_14: Multi-label learning deals with data associated with multiple labels simultaneously. Previous work on multi-label learning assumes that for each instance, the "full" label set associated with each training instance is given by users. In many applications, however, to get the full label set for each instance is difficult and only a "partial" set of labels is available. In such cases, the appearance of a label means that the instance is associated with this label, while the absence of a label does not imply that this label is not proper for the instance. We call this kind of problem "weak label" problem. In this paper, we propose the WELL (WEak Label Learning) method to solve the weak label problem. We consider that the classification boundary for each label should go across low density regions, and that each label generally has much smaller number of positive examples than negative examples. The objective is formulated as a convex optimization problem which can be solved efficiently. Moreover, we exploit the correlation between labels by assuming that there is a group of low-rank base similarities, and the appropriate similarities between instances for different labels can be derived from these base similarities. Experiments validate the performance of WELL.
  @cite_1: In this paper, we study the problem of mining partially annotated images. We first define what the problem of mining partially annotated images is, and argue that in many real-world applications annotated images are typically partially annotated and thus that the problem of mining partially annotated images exists in many situations. We then propose an effective solution to this problem based on a statistical model we have developed called the Semi-Supervised Correspondence Hierarchical Dirichlet Process (SSCHDP). The main idea of this model lies in exploiting the information pertaining to partially annotated images or even unannotated images to achieve semi-supervised learning under the HDP structure. We apply this model to completing the annotations appropriately for partially annotated images in the training data and then to predicting the annotations appropriately and completely for all the unannotated images either in the training data or in any unseen data beyond the training process. Experiments show that SSC-HDP is superior to the peer models from the recent literature when they are applied to solving the problem of mining partially annotated images.
  @cite_12: We consider a special type of multi-label learning where class assignments of training examples are incomplete. As an example, an instance whose true class assignment is (c 1 , c 2 , c 3 ) is only assigned to class c 1 when it is used as a training sample. We refer to this problem as multi-label learning with incomplete class assignment. Incompletely labeled data is frequently encountered when the number of classes is very large (hundreds as in MIR Flickr dataset) or when there is a large ambiguity between classes (e.g., jet vs plane). In both cases, it is difficult for users to provide complete class assignments for objects. We propose a ranking based multi-label learning framework that explicitly addresses the challenge of learning from incompletely labeled data by exploiting the group lasso technique to combine the ranking errors. We present a learning algorithm that is empirically shown to be efficient for solving the related optimization problem. Our empirical study shows that the proposed framework is more effective than the state-of-the-art algorithms for multi-label learning in dealing with incompletely labeled data.
summary: The work in @cite_19 studied the large-scale multi-label leanring problems, and proposed an efficient approach M3L. In addition, multi-label learning with incomplete label assignment has also been studied on small moderate-size datasets @cite_14 @cite_12 @cite_1 . The work in @cite_14 proposed an approach to infer missing labels in multi-label learning under transductive settings.