In partial label learning (PLL), every sample is associated with a candidate label set comprising the ground-truth label and several noisy labels. The conventional PLL assumes the noisy labels are randomly generated (instance-independent), while in practical scenarios, the noisy labels are always instance-dependent and are highly related to the sample features, leading to the instance-dependent partial label learning (IDPLL) problem. Instance-dependent noisy label is a double-edged sword. On one side, it may promote model training as the noisy labels can depict the sample to some extent. On the other side, it brings high label ambiguity as the noisy labels are quite undistinguishable from the ground-truth label. To leverage the nuances of IDPLL effectively, for the first time we create class-wise embeddings for each sample, which allow us to explore the relationship of instance-dependent noisy labels, i.e., the class-wise embeddings in the candidate label set should have high similarity, while the class-wise embeddings between the candidate label set and the non-candidate label set should have high dissimilarity. Moreover, to reduce the high label ambiguity, we introduce the concept of class prototypes containing global feature information to disambiguate the candidate label set. Extensive experimental comparisons with twelve methods on six benchmark data sets, including four fine-grained data sets, demonstrate the effectiveness of the proposed method. The code implementation is publicly available at https://github.com/Yangfc-ML/CEL.

This paper studies the semi-supervised partial label learning (SSPLL) problem, which aims to improve the partial label learning (PLL) by leveraging unlabeled samples. Both the existing SSPLL methods and the semi-supervised learning methods exploit the information in unlabeled samples by selecting high-confidence unlabeled samples as the pseudo labels based on the maximum value of the model output. However, the scarcity of labeled samples and the ambiguity from partial labels skew this strategy towards an unfair selection of high-confidence samples on each class, most notably during the initial phases of training, resulting in slower training and performance degradation. In this paper, we propose a novel method FairMatch, which adopts a learning state aware self-adaptive threshold for selecting the same number of high-confidence samples on each class, and uses augmentation consistency to incorporate the unlabeled samples to promote PLL. In addition, we adopt the candidate label disambiguation to utilize the partial labeled samples and mix up the partial labeled samples and the selected high-confidence unlabeled samples to prevent the model from overfitting on partial label samples. FairMatch can achieve maximum accuracy improvements of 9.53%, 4.9%, and 16.45% on CIFAR-10, CIFAR-100, and CIFAR-100H, respectively. The codes can be found at https://github.com/jhjiangSEU/FairMatch.

This paper addresses the problem of partial multi-label learning (PML), a challenging weakly supervised learning framework, where each sample is associated with a candidate label set comprising both ground-true labels and noisy labels. We theoretically reveal that an increased number of noisy labels in the candidate label set leads to an enlarged generalization error bound, consequently degrading the classification performance. Accordingly, the key to solving PML lies in accurately removing the noisy labels within the candidate label set. To achieve this objective, we leverage prior knowledge about the noisy labels in PML, which suggests that they only exist within the candidate label set and possess binary values. Specifically, we propose a constrained regression model to learn a PML classifier and select the noisy labels. The constraints of the model strictly enforce the location and value of the noisy labels. Simultaneously, the supervision information provided by the candidate label set is unreliable due to the presence of noisy labels. In contrast, the non-candidate labels of a sample precisely indicate the classes to which the sample does not belong. To aid in the selection of noisy labels, we construct a competitive classifier based on the non-candidate labels. The PML classifier and the competitive classifier form a competitive relationship, encouraging mutual learning. We formulate the proposed model as a discrete optimization problem to effectively remove the noisy labels, and we solve it using an alternative algorithm. Extensive experiments conducted on 6 real-world partial multi-label data sets and 7 synthetic data sets, employing various evaluation metrics, demonstrate that our method significantly outperforms state-of-the-art PML methods. The code implementation is publicly available at https://github.com/Yangfc-ML/NLR.

Exploiting label correlations is important to multi-label classification. Previous methods capture the high-order label correlations mainly by transforming the label matrix to a latent label space with low-rank matrix factorization. However, the label matrix is generally a full-rank or approximate full-rank matrix, making the low-rank factorization inappropriate. Besides, in the latent space, the label correlations will become implicit. To this end, we propose a simple yet effective method to depict the high-order label correlations explicitly, and at the same time maintain the high-rank of the label matrix. Moreover, we estimate the label correlations and infer model parameters simultaneously via the local geometric structure of the input to achieve mutual enhancement. Comparative studies over twelve benchmark data sets validate the effectiveness of the proposed algorithm in multi-label classification. The exploited high-order label correlations are consistent with common sense empirically.Our code is publicly available athttps://github.com/Chongjie-Si/HOMI.

Multi-label ensemble can achieve superior performance on multi-label learning problems by integrating a number of base classifiers. In existing multi-label ensemble methods, the base classifiers are usually trained with the same original features; it is difficult for each base classifier to capture label-relevant or label subset-relevant information. Meanwhile, the manually designed integrating strategies cannot automatically distinguish the importance of the base classifiers, which also lack flexibility and scalability. In order to resolve these problems, this paper proposes a new multi-label ensemble framework, named Attention-aware Multi-label Ensemble with Label Subset-specIfic FeaTures (AME-LSIFT). It utilizes$c$-means clustering to produce Label Subset-specIfic FeaTures (LSIFT), constructs a neural network based model for each label subset, and integrates the base models with a dynamic and automatic attention-aware mechanism. Moreover, an objective function that considers both the label subset accuracy and ensemble accuracy is developed for training the proposed AME-LSIFT. Experiments conducted on ten benchmark datasets demonstrate the superior performance of the proposed method compared with state-of-the-art approaches.

Partial label learning (PLL) is a significant weakly supervised learning framework, where each training example corresponds to a set of candidate labels among which only one is the ground-truth label. Existing works on partial label dimensionality reduction only exploit the disambiguated labels, but overlook the available semantic dissimilarity relationship hidden in the disambiguated labeling confidence, i.e., the smaller the inner product of the labeling confidences of two instances, the less likely they have the same ground-truth label. By combining such global dissimilarity relationship with local neighborhood information, we propose a novel partial label dimensionality reduction method named SDLPP, which employs an alternating procedure including candidate label disambiguation, semantic dissimilarity generation and dimensionality reduction. The labeling confidences of candidate labels and semantic dissimilarity relationship are constantly updated through the alternating procedure, where the processes in each iteration are based on the low-dimensional data obtained in the previous iteration. After the alternating procedure, SDLPP maps the original data to a pre-specified low-dimensional feature space. Comprehensive experiments on both synthetic and real-world data sets validate that SDLPP can improve the generalization performance of different PLL algorithms, and outperform state-of-the-art partial label dimensionality reduction methods. The codes can be publicly accessible on the link https://github.com/jhjiangSEU/SDLPP.

In partial label learning (PLL), each training sample is associated with a set of candidate labels, among which only one is valid. The core of PLL is to disambiguate the candidate labels to get the ground-truth one. In disambiguation, the existing works usually do not fully investigate the effectiveness of the non-candidate label set (a.k.a. complementary labels), which accurately indicates a set of labels that do not belong to a sample. In this paper, we use the non-candidate labels to induce a complementary classifier, which naturally forms an adversarial relationship against the traditional PLL classifier, to eliminate the false-positive labels in the candidate label set. Besides, we assume the feature space and the label space share the same local topological structure captured by a dynamic graph, and use it to assist disambiguation. Extensive experimental results validate the superiority of the proposed approach against state-of-the-art PLL methods on 4 controlled UCI data sets and 6 real-world data sets and reveal the usefulness of complementary learning in PLL. The code has been released in the link https://github.com/Chongjie-Si/PL-CL