Label distribution learning (LDL) is a novel machine learning paradigm that gives a description degree of each label to a particular instance. But many existing datasets contain only simple logical labels, since it is difficult and time-consuming to directly obtain the label distribution. So label enhancement (LE) is proposed to convert multi-label datasets consisting of logical labels into label distribution datasets. In recently, many LE algorithms have been proposed and most of them concentrate on the fitting degree, but ignore the ordering relation between positive and negative labels. Therefore, in this paper, we propose an LE algorithm based on maintaining positive and negative label relation, which contains a novel ranking loss that can generate different penalties according to different ranking errors. Our algorithm achieves a good balance between the degree of fitting and the ordering relation. The experimental results on several real-world datasets validate the effectiveness of our method.

Label distribution learning (LDL) is a novel machine learning paradigm that can be seen as an extension of multi-label learning (MLL). Compared with MLL, the advantages of LDL are reflected in the following perspectives: (1) the label distribution gives the relevance description of each label to unknown instances in quantitative terms; (2) the distribution implicitly gives the relevance intensities relation of different labels to a particular instance in qualitative terms, i.e., the label ranking relation. All existing LDL models aim to fit the ground-truth label distribution by quantitatively minimizing the distance between distributions or maximizing the similarity between distributions, which only uses the first advantage of the label distribution but ignores the label ranking relation, which may lose some useful semantic information implied in the label distribution, thus reducing the performance of LDL. Therefore, we propose a novel algorithm to solve this problem by introducing the ranking loss function to LDL. In addition, in order to evaluate the LDL algorithms more comprehensively and verify that the ranking loss is beneficial for keeping the label ranking relation, we also introduce two popular ranking evaluation metrics for LDL. The experimental results on 13 real-world datasets validate the effectiveness of our method.

Hashing methods have captured much attention for cross-modal retrieval in recent years. Most existing approaches mainly focus on preserving the semantic similarity across heterogeneous modalities in a shared Hamming subspace, while the label information and potential correlations of multi-label semantics are not fully excavated. In this article, a novel Adaptive Label correlation based asymmEtric Cross-modal Hashing method, i.e., ALECH, is proposed for cross-modal retrieval. ALECH decomposes hash learning into two steps, hash codes learning and hash functions learning. For hash codes learning, the high-order semantic label correlations are adaptively exploited to guide the latent feature learning, while simultaneously generating the binary codes in a discrete manner. The asymmetric strategy is utilized to connect the latent feature space and Hamming space, and preserve the pairwise semantic similarity. Different from other two-step methods that directly adopt simple least-squares regression to learn hash functions based on binary codes, ALECH leverages both hash codes and semantic labels for hash functions learning which further preserves the similarity. Experiments on several benchmark datasets demonstrate that the proposed ALECH method outperforms the state-of-the-art cross-hashing methods.

Label distribution learning (LDL) is proposed for solving the label ambiguity problem in recent years, which can be seen as an extension of multi-label learning. To improve the performance of label distribution learning, some existing algorithms exploit label correlations in a global manner that assumes the label correlations are shared by all instances. However, the instances in different groups may share different label correlations, and few label correlations are globally applicable in real-world tasks. In this paper, two novel label distribution learning algorithms are proposed by exploiting label correlations on local samples, which are called GD-LDL-SCL and Adam-LDL-SCL, respectively. To utilize the label correlations on local samples, the influence of local samples is encoded, and a local correlation vector is designed as the additional features for each instance, which is based on the different clustered local samples. Then, the label distribution for an unseen instance can be predicted by exploiting the original features and the additional features simultaneously. Extensive experiments on some real-world data sets validate that our proposed methods can address the label distribution problems effectively and outperform state-of-the-art methods.