Multi-Label Learning (MLL) involves the task of assigning a set of relevant labels to a given instance. Recently, Label Enhancement (LE) has gained significant attention in various MLL tasks, as it allows for effective mining the implicit relative importance information of different labels. However, in existing LE-based MLL methods, the LE process is decoupled from the MLL process. Consequently, the label distribution recovered by the LE process may not be suitable for training the predictive model, thus affecting the overall learning system. In this study, we propose a novel approach named interactive Fusion Label Enhancement for Multi-Label Learning ( Flem ) that seamlessly integrates the LE process with the MLL process. Specifically, we introduce a matching and interaction mechanism comprising a novel interaction label enhancement loss and a contrastive alignment approach to prevent object mismatch. Furthermore, we present a unified label distribution loss that establishes the relationship between the recovered label distribution and the training of the predictive model. By leveraging these losses, the label distributions obtained from the LE process can be efficiently utilized for training the predictive model. Experimental results on multiple benchmark datasets demonstrate the effectiveness of the proposed method.

Multi-label text classification (MLTC) refers to the problem of tagging a given document with the most relevant subset of labels. One of the biggest challenges for MLTC is the existence of class imbalance. Most advanced MLTC models suffer from this issue, which limits the performance of the models. In this paper, we propose a model-agnostic framework named variational continuous label distribution learning (VCLDL) to address this problem. VCLDL theoretically builds a corresponding relationship between the feature space and the label space to mine the information hidden in the observable logical labels. Specifically, VCLDL regards label distribution as a continuous density function in latent space and forms a flexible variational approach to approximate the density function of the labels with the collaboration of the feature space. Combined with VCLDL, MLTC models can pay more attention to the distribution of the whole label set, rather than specific labels with maximum response values, thus the class imbalance problem can be well overcome. Experimental results on multiple benchmark datasets demonstrate that VCLDL can bring significant performance improvements over the existing MLTC models.

Label Distribution Learning (LDL) has attracted increasing research attentions due to its potential to address the label ambiguity problem in machine learning and success in many real-world applications. In LDL, it is usually expensive to obtain the ground-truth label distributions of data, but it is relatively easy to obtain the logical labels of data. How to use training instances only with logical labels to learn an effective LDL model is a challenging problem. In this paper, we propose a two-step framework to address this problem. Specifically, we firstly design an efficient recovery model to recover the latent label distributions of training instances, named Fast Label Enhancement (FLE). Our idea is to use non-negative matrix factorization (NMF) to mine the label distribution information from the feature space. Moreover, we take the instance-class similarities into consideration to discover the importance of each label to training instances, which is useful for learning precise label distributions. Then, we train a predictive model for testing instances based on generated label distributions of training instances and an existing LDL method (e.g., SA-BFGS). Experimental results on fifteen benchmark datasets show the effectiveness of the proposed two-step framework and verify the superiority of FLE over several state-of-the-art approaches.

Multi-view partial multi-label learning (MVPML) aims to learn a multi-label predictive model from the training examples, each of which is presented by multiple feature vectors while associated with a set of candidate labels where only a subset is correct. Generally, existing techniques work simply by identifying the ground-truth label via aggregating the features from all views to train a final classifier, but ignore the cause of the incorrect labels in the candidate label sets, i.e., the diverse property of the representation from different views leads to the incorrect labels which form the candidate labels alone with the essential supervision. In this paper, a novel MVPML approach is proposed to learn the predictive model and the incorrect-labeling model jointly by incorporating the graph-fusion-based topological structure of the feature space. Specifically, the latent label distribution and the incorrect labels are identified simultaneously in a unified framework under the supervision of candidate labels. In addition, a common topological structure of the feature space from all views is learned via the graph fusion for further capturing the latent label distribution. Experimental results on the real-world datasets clearly validate the effectiveness of the proposed approach for solving multi-view partial multi-label learning problems.

Label distribution is more general than both single-label annotation and multi-label annotation. It covers a certain number of labels, representing the degree to which each label describes the instance. The learning process on the instances labeled by label distributions is called label distribution learning (LDL). Unfortunately, many training sets only contain simple logical labels rather than label distributions due to the difficulty of obtaining the label distributions directly. To solve this problem, one way is to recover the label distributions from the logical labels in the training set via leveraging the topological information of the feature space and the correlation among the labels. Such process of recovering label distributions from logical labels is defined as label enhancement (LE), which reinforces the supervision information in the training sets. This paper proposes a novel LE algorithm called Graph Laplacian Label Enhancement (GLLE). Experimental results on one artificial dataset and fourteen real-world LDL datasets show clear advantages of GLLE over several existing LE algorithms. Furthermore, experimental results on eleven multi-label learning datasets validate the advantage of GLLE over the state-of-the-art multi-label learning approaches.

The task of multi-label learning is to predict a set of relevant labels for the unseen instance. Traditional multi-label learning algorithms treat each class label as a logical indicator of whether the corresponding label is relevant or irrelevant to the instance, i.e., +1 represents relevant to the instance and -1 represents irrelevant to the instance. Such label represented by -1 or +1 is called logical label. Logical label cannot reflect different label importance. However, for real-world multi-label learning problems, the importance of each possible label is generally different. For the real applications, it is difficult to obtain the label importance information directly. Thus we need a method to reconstruct the essential label importance from the logical multilabel data. To solve this problem, we assume that each multi-label instance is described by a vector of latent real-valued labels, which can reflect the importance of the corresponding labels. Such label is called numerical label. The process of reconstructing the numerical labels from the logical multi-label data via utilizing the logical label information and the topological structure in the feature space is called Label Enhancement. In this paper, we propose a novel multi-label learning framework called LEMLL, i.e., Label Enhanced Multi-Label Learning, which incorporates regression of the numerical labels and label enhancement into a unified framework. Extensive comparative studies validate that the performance of multi-label learning can be improved significantly with label enhancement and LEMLL can effectively reconstruct latent label importance information from logical multi-label data.