Multiple complementary-label learning (MCLL) is a machine learning task that involves learning a classifier from instances with multiple complementary labels (MCLs). MCLs are labels that indicate the incorrect labels of an instance. Previous methods for learning with ambiguous supervised information may not be effective because MCLs only make up a small proportion of all labels. In this article, we propose MulCo, a simple yet effective framework that uses contrastive learning to enhance the representation capability in MCLL. Contrastive learning involves contrasting semantically similar and dissimilar pairs of instances, with the goal of benefiting from negatives whose ground-truth labels differ from those of anchors. However, it is possible for dissimilar pairs to have the same label due to the random sampling of negatives from inaccurately labeled data. To solve this problem, we design a sifted contrastive loss for MulCo to correct the sampling of same-label negative pairs. We also provide theoretical evidence for the feasibility of the sifted contrastive loss by establishing an upper bound on the ideal contrastive loss. Correspondingly, we develop two progressive solutions using the properties of complementary labels to approximate the ideal contrastive loss through weighting. Our empirical study demonstrates the effectiveness of the proposed method. The code of this article is available at https://github.com/gaoyi439/MulCo .

In multi-label classification, each instance can be associated with multiple class labels simultaneously. However, each label is supposed to possess specific characteristics of its own and thus naturally shows distinct discriminative preferences on features. With consideration on this property, label-specific feature learning has emerged as a promising strategy for multi-label classification, which constructs features specific to each label to facilitate multi-label discrimination process. This article aims to provide a timely review on this emerging modeling strategy, focusing on main progress made during the last decade. Firstly, fundamentals on label-specific feature learning including formal definition and key challenges are provided. Then, six representative label-specific feature learning algorithms are scrutinized under a concise taxonomy with necessary discussions on algorithmic properties. Lastly, open research problems in label-specific feature learning are summarized to provide possible directions for future studies.

Label-specific features work as an effective supervised feature manipulation strategy to account for distinct discriminative properties of each class label in multi-label classification. Existing approaches implement this strategy in its primal form, i.e., finding the most pertinent features specific to each class label and directly inducing classifiers on these features. Instead of such a straightforward implementation, a dual perspective for label-specific feature learning is investigated in this article. As a dual problem of existing primal one, we consider label-specific discriminative properties by identifying non-informative features for each class label and making the discrimination process immutable to variations of identified features. Accordingly, a perturbation-based approach Dela is presented, which endows classifiers with immutability on simultaneously identified non-informative features by solving a probabilistically relaxed expected risk minimization problem. Furthermore, we touch the realistic issue of label-specific feature learning in a weakly supervised scenario via extending Dela to accommodate to multi-label data with missing labels. Comprehensive experiments show that our approach outperforms the state-of-the-art counterparts.

Multi-instance partial-label learning (MIPL) tackles scenarios where each training sample is represented as a multiinstance bag associated with a candidate label set. This set contains one true label and several false positives. Existing MIPL algorithms have predominantly focused on mapping multiinstance bags to candidate label sets for disambiguation. However, these algorithms may not be adequately generalizable in intricate real-world situations due to their reliance on heuristic methods for identifying true labels. In this paper, we propose PROMIPL, i.e., a PRObabilistic generative model for Multi-instance partiallabel learning, to address these challenges. PROMIPL is the first attempt to explore the probabilistic generative model to infer latent ground-truth labeling information from the data generation process in multi-instance partial-label learning. Besides, the discovered underlying structures also provide improved explanations of the classification predictions. To circumvent the computationally intensive process of training the generative model, we formulate a unified variational lower bound within the stochastic gradient variational Bayesian framework for the model parameters. Experimental results from benchmark and realworld datasets show that our proposed PROMIPL is competitive or superior to the state-of-the-art methods.

Multiple-instance learning (MIL) solves the problem where training instances are grouped in bags, and a binary (positive or negative) label is provided for each bag. Most of the existing MIL studies need fully labeled bags for training an effective classifier, while it could be quite hard to collect such data in many real-world scenarios, due to the high cost of data labeling process. Fortunately, unlike fully labeled data, triplet comparison data can be collected in a more accurate and human-friendly way. Therefore, in this article, we for the first time investigate MIL from only triplet comparison bags , where a triplet (X a , X b , X c ) contains the weak supervision information that bag X a is more similar to X b than to X c . To solve this problem, we propose to train a bag-level classifier by the empirical risk minimization framework and theoretically provide a generalization error bound. We also show that a convex formulation can be obtained only when specific convex binary losses such as the square loss and the double hinge loss are used. Extensive experiments validate that our proposed method significantly outperforms other baselines.

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.

Numerous researches have proved that deep neural networks (DNNs) can fit almost everything even given data with noisy labels, and result in poor generalization performance. However, recent studies suggest that DNNs tend to gradually memorize the data, moving from correct data to mislabeled data. Inspired by this finding, we propose a novel method namedDynamic Loss Thresholding (DLT). During the training process, DLT records the loss value of each sample and calculates dynamic loss thresholds. Specifically, DLT compares the loss value of each sample with the current loss threshold. Samples with smaller losses can be considered as clean samples with higher probability and vice versa. Then, DLT discards the potentially corrupted labels and further leverages self-training semi-supervised learning techniques. Experiments on CIFAR-10/100, WebVision and Clothing1M demonstrate substantial improvements over recent state-of-the-art methods. In addition, we investigate two real-world problems. Firstly, we propose a novel approach to estimate the noise rates of datasets based on the loss difference between the early and late training stages of DNNs. Secondly, we explore the effect of hard samples (which are difficult to be distinguished) on the process of learning from noisy labels.

Partial label learning learns from instances with weak supervision, where each instance is associated with a set of candidate labels, among which only one is valid. Recently, dimensionality reduction has emerged as an effective preprocessing strategy to improve generalization performance. Existing approaches mainly tackle this problem through supervised or unsupervised dimensionality reduction. However, the former requires ground-truth labels, which are concealed in candidate label sets. Consequently, methods in this line may suffer from overfitting due to false positive labels in candidate label set. Conversely, the latter overlooks weakly supervised information in training instances, leading to performance degradation. In this paper, we propose an approach calledpartial label Dimensionality Reduction via Adaptive Weight (Draw)to leverage the strengths of Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA). Specifically, our approach tends to exploit unsupervised and data-driven nature of PCA to capture underlying structure of instances in initial stage. As the ground-truth label is gradually identified, our method increasingly relies on the discriminative ability of LDA to enhance the separation between different classes. Through extensive experiments on diverse partial label datasets, we validate that the proposed dimensionality reduction approach significantly improves classification performance of well-established partial label learning algorithms.

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

In multi-dimensional classification (MDC), a number of class variables are assumed in the output space with each of them specifying the class membership w.r.t. one heterogeneous class space. One major challenge in learning from MDC examples lies in the heterogeneity of class spaces, where the modeling outputs from different class spaces are not directly comparable. To tackle this problem, we propose a new strategy nameddecomposed label encoding,which enables modeling alignment for MDC in an encoded label space derived from one-versus-one (OvO) decomposition. Specifically, the original MDC output space is transformed into a ternary encoded label space by conducting OvO decomposition w.r.t. each class space. Then, the manifold structure in the feature space is exploited to enrich the labeling information in the encoded label space. Finally, the predictive model is induced by fitting the metric-aligned modeling outputs with enriched labeling information. Extensive experiments over twenty benchmark data sets clearly show the superiority of the proposed MDC strategy against state-of-the-art approaches.

Partial label learning induces a multi-class classifier from training examples each associated with a candidate label set where the ground-truth label is concealed. Feature selection improves the generalization ability of learning system via selecting essential features for classification from the original feature set, while the task of partial label feature selection is challenging due to ambiguous labeling information. In this paper, the first attempt towards partial label feature selection is investigated via mutual-information-based dependency maximization. Specifically, the proposed approach SAUTE iteratively maximizes the dependency between selected features and labeling information, where the value of mutual information is estimated from confidence-based latent variable inference. In each iteration, the near-optimal features are selected greedily according to properties of submodular mutual information function, while the density of latent label variable is inferred with the help of updated labeling confidences over candidate labels by resorting to kNN aggregation in the induced lower-dimensional feature space. Extensive experiments over synthetic as well as real-world partial label data sets show that the generalization ability of well-established partial label learning algorithms can be significantly improved after coupling with the proposed feature selection approach.

Partial label learning is a weakly supervised learning framework where each training example is associated with multiple candidate labels, among which only one is valid. Existing works on partial label learning mainly focus on classification model induction by disambiguating candidate label sets in the output space. Nevertheless, the feature representations of partial label training examples may be less informative of the ground-truth labels, which may result in negative influences on the disambiguation process. To circumvent this difficulty, the first attempt towards discrimination augmentation for partial label learning is investigated in this paper. The feature space is enriched with confidence-rated class prototype features to replenish discriminative characteristics of the underlying ground-truth labels for partial label training examples. Specially, an optimization formulation is proposed to jointly optimize the class prototype and estimate the labeling confidence over partial label training examples, which enforces both global consistency in the feature space and local consistency in the label space. We show that the class prototypes and the labeling confidence can be solved via alternating optimization. Extensive experiments on synthetic as well as real-world data sets validate the effectiveness of the proposed approach for improving the generalization performance of state-of-the-art partial label learning algorithms.

For Virtual Desktop Infrastructure (VDI) system, effective resource management is rather important where turning off spare virtual machines would help save running cost while maintaining sufficient virtual machines is essential to secure satisfactory user experience. Current VDI resource management strategy works in apassivemanner by either reactively driving available capacity based on user demands or following manually configured schedules, which may lead to unnecessary running costs or unsatisfactory user experience. In this article, we propose a first attempt toward proactive VDI resource management, where two adaptive learning approaches for VDI workload prediction are proposed by learning from multi-grained historical features. Fornon-persistentdesktop pool, based on the aggregation session count of pool-sharing users, theCAFEapproach induces a pool-level workload predictive model by utilizing coarse-to-fine historical features extracted from aggregation workload data. Forpersistentdesktop pool, based on the session connection status of individual users within the same pool, theSOUPapproach induces user-level workload predictive model by incorporating encoded multi-grained features extracted from the logon behavior of individual users into an aggregation pool-level model. Extensive experiments on datasets of real VDI customers and electricity load evidently verify the effectiveness of the proposed adaptive approaches for VDI workload prediction as well as other workload prediction tasks.

In multi-label classification, the task is to induce predictive models which can assign a set of relevant labels for the unseen instance. The strategy of label-specific features has been widely employed in learning from multi-label examples, where the classification model for predicting the relevancy of each class label is induced based on its tailored features rather than the original features. Existing approaches work by generating a group of tailored features for each class label independently, where label correlations are not fully considered in the label-specific features generation process. In this article, we extend existing strategy by proposing a simple yet effective approach based on BiLabel-specific features. Specifically, a group of tailored features is generated for a pair of class labels with heuristic prototype selection and embedding. Thereafter, predictions of classifiers induced by BiLabel-specific features are ensembled to determine the relevancy of each class label for unseen instance. To thoroughly evaluate the BiLabel-specific features strategy, extensive experiments are conducted over a total of 35 benchmark datasets. Comparative studies against state-of-the-art label-specific features techniques clearly validate the superiority of utilizing BiLabel-specific features to yield stronger generalization performance for multi-label classification.

As an emerging weakly supervised learning framework, partial label learning considers inaccurate supervision where each training example is associated with multiple candidate labels among which only one is valid. In this article, a first attempt toward employing dimensionality reduction to help improve the generalization performance of partial label learning system is investigated. Specifically, the popular linear discriminant analysis (LDA) techniques are endowed with the ability of dealing with partial label training examples. To tackle the challenge of unknown ground-truth labeling information, a novel learning approach named Delin is proposed which alternates between LDA dimensionality reduction and candidate label disambiguation based on estimated labeling confidences over candidate labels. On one hand, the (kernelized) projection matrix of LDA is optimized by utilizing disambiguation-guided labeling confidences. On the other hand, the labeling confidences are disambiguated by resorting to k NN aggregation in the LDA-induced feature space. Extensive experiments over a broad range of partial label datasets clearly validate the effectiveness of Delin in improving the generalization performance of well-established partial label learning algorithms.

Partial label learning deals with training examples each associated with a set of candidate labels, among which only one is valid. Most existing works focus on manipulating the label space by estimating the labeling confidences of candidate labels, while the task of manipulating the feature space by dimensionality reduction has been rarely investigated. In this paper, a novel partial label dimensionality reduction approach named CENDA is proposed via confidence-based dependence maximization. Specifically, CENDA adapts the Hilbert-Schmidt Independence Criterion (HSIC) to help identify the projection matrix, where the dependence between projected feature information and confidence-based labeling information is maximized iteratively. In each iteration, the projection matrix admits closed-form solution by solving a tailored generalized eigenvalue problem, while the labeling confidences of candidate labels are updated by conducting kNN aggregation in the projected feature space. Extensive experiments over a broad range of benchmark data sets show that the predictive performance of well-established partial label learning algorithms can be significantly improved by coupling with the proposed dimensionality reduction approach.

Stroke evaluation is critical for coaches to evaluate players' performance in table tennis matches. However, current methods highly demand proficient knowledge in table tennis and are time-consuming. We collaborate with the Chinese national table tennis team and propose Tac-Valuer, an automatic stroke evaluation framework for analysts in table tennis teams. In particular, to integrate analysts' knowledge into the machine learning model, we employ the latest effective framework named abductive learning, showing promising performance. Based on abductive learning, Tac-Valuer combines the state-of-the-art computer vision algorithms to extract and embed stroke features for evaluation. We evaluate the design choices of the approach and present Tac-Valuer's usability through use cases that analyze the performance of the top table tennis players in world-class events.

Multi-label learning deals with training examples each represented by a single instance while associated with multiple class labels, and the task is to train a predictive model which can assign a set of proper labels for the unseen instance. Existing approaches employ the common assumption of equal labeling-importance, i.e., all associated labels are regarded to be relevant to the training instance while their relative importance in characterizing its semantics are not differentiated. Nonetheless, this common assumption does not reflect the fact that the importance degree of each relevant label is generally different, though the importance information is not directly accessible from the training examples. In this article, we show that it is beneficial to leverage the implicit relative labeling-importance (RLI) information to help induce multi-label predictive model with strong generalization performance. Specifically, RLI degrees are formalized as multinomial distribution over the label space, which can be estimated by either global label propagation procedure or local k-nearest neighbor reconstruction. Correspondingly, the multi-label predictive model is induced by fitting modeling outputs with estimated RLI degrees along with multi-label empirical loss regularization. Extensive experiments clearly validate that leveraging implicit RLI information serves as a favorable strategy to achieve effective multi-label learning.

In conventional multi-label learning framework, each example is assumed to be represented by a single feature vector and associated with multiple valid labels simultaneously. Nonetheless, real-world objects usually exhibit complicated properties which can have multi-view feature representation as well as false positive labeling. Accordingly, the problem of multi-view partial multi-label learning (MVPML) is studied in this paper, where each example is assumed to be presented by multiple feature vectors while associated with multiple candidate labels which are only partially valid. To learn from MVPML examples, a novel approach named FIMAN is proposed which makes use of multi-view feature representation to tackle the noisy labeling information. Firstly, an aggregate manifold structure over training examples is generated by adaptively fusing affinity information conveyed by feature vectors of different views. Then, candidate labels of each training example are disambiguated by preserving the feature-induced manifold structure in label space. Finally, the resulting predictive models are learned by fitting modeling outputs with the disambiguated labels. Extensive experiments on a number of real-world data sets show that FIMAN achieves highly competitive performance against state-of-the-art approaches in solving the MVPML problem.

Partial label learning aims to induce a multi-class classifier from training examples where each of them is associated with a set of candidate labels, among which only one is the ground-truth label. The common strategy to train predictive model is disambiguation, i.e. differentiating the modeling outputs of individual candidate labels so as to recover ground-truth labeling information. Recently, feature-aware disambiguation was proposed to generate different labeling confidences over candidate label set by utilizing the graph structure of feature space. However, the existence of noise and outliers in training data makes the similarity derived from original features less reliable. To this end, we proposed a novel approach for partial label learning based on adaptive graph guided disambiguation (PL-AGGD). Compared with fixed graph, adaptive graph could be more robust and accurate to reveal the intrinsic manifold structure within the data. Moreover, instead of the two-stage strategy in previous algorithms, our approach performs label disambiguation and predictive model training simultaneously. Specifically, we present a unified framework which jointly optimizes the ground-truth labeling confidences, similarity graph and model parameters to achieve strong generalization performance. Extensive experiments show that PL-AGGD performs favorably against state-of-the-art partial label learning approaches.

Partial label learning is an emerging weakly-supervised learning framework where each training example is associated with multiple candidate labels among which only one is valid. Dimensionality reduction serves as an effective way to help improve the generalization ability of learning system, while the task of partial label dimensionality reduction is challenging due to the unknown ground-truth labeling information. In this paper, the first attempt towards partial label dimensionality reduction is investigated by endowing the popular linear discriminant analysis (LDA) techniques with the ability of dealing with partial label training examples. Specifically, a novel learning procedure named DELIN is proposed which alternates between LDA dimensionality reduction and candidate label disambiguation based on estimated labeling confidences over candidate labels. On one hand, the projection matrix of LDA is optimized by utilizing disambiguation-guided labeling confidences. On the other hand, the labeling confidences are disambiguated by resorting to kNN aggregation in the LDA-induced feature space. Extensive experiments on synthetic as well as real-world partial label data sets clearly validate the effectiveness of DELIN in improving the generalization ability of state-of-the-art partial label learning algorithms.

As a practical problem in open and dynamic environments, class-incremental learning has attracted much attention from many fields. Learning with augmented class (LAC) problem formulates one of the core difficulties of class-incremental learning: instances of augmented class need to be predicted with the restriction that only examples from seen classes are observed in training phase. LACU framework advances the study of LAC problem by exploiting unlabeled data, while it does not take into account an important practical problem widely-existing in real-world applications of LAC - imbalanced class distributions among seen classes, which will further increase the learning difficulties of LAC problem. We propose a novel approach Label Confidence Propagation (LCP) to tackle the problem of imbalanced augmented class learning with unlabeled data. LCP enlarges the labeled training data set by estimating class labels for unlabeled data, to meet the challenge of lacking supervision information of augmented classes via identifying some of their instances, and to alleviate the damage of class-imbalance via identifying more instances for each seen class. LCP firstly initializes label confidence, i.e., the posterior probability distributions of all classes (including augmented classes) for unlabeled data, then iteratively propagates label confidence to identify a valid label for each unlabeled instance to enlarge the labeled training data set. Finally, LCP predicts for unseen instances by linear neighborhood reconstruction to be robust to potential noise. Results on abundant experiments show that LCP is significantly superior to many state-of-the-art methods, and robust to high imbalance ratio and high open level. LCP can sufficiently unleash its strength especially when there are abundant unlabeled data available.

Partial label (PL) learning aims to induce a multi-class classifier from training examples where each of them is associated with a set of candidate labels, among which only one is valid. It is well-known that the problem of class-imbalance stands as a major factor affecting the generalization performance of multi-class classifier, and this problem becomes more pronounced as the ground-truth label of each PL training example is not directly accessible to the learning approach. To mitigate the negative influence of class-imbalance to partial label learning, a novel class-imbalance aware approach named CIMAP is proposed by adapting over-sampling techniques for handling PL training examples. Firstly, for each PL training example, CIMAP disambiguates its candidate label set by estimating the confidence of each class label being ground-truth one via weighted k-nearest neighbor aggregation. After that, the original PL training set is replenished for model induction by over-sampling existing PL training examples via manipulation of the disambiguation results. Extensive experiments on artificial as well as real-world PL data sets show that CIMAP serves as an effective data-level approach to mitigate the class-imbalance problem for partial label learning.

Multi-label learning deals with objects with rich semantics where each example is associated with multiple class labels simultaneously. Intuitively, each class label is supposed to possess specific characteristics of its own. Therefore, exploiting label-specific features serves as one of the promising techniques to learn from multi-label examples. Specifically, the LIFT approach generates the label-specific features by clustering the multi-label training examples in a label-wise style, which ignores the utilization of label correlations to improve generalization performance. In this paper, a new multi-label learning method named LIFTACE (multi-label learning with Label-specIfic FeaTures viA Clustering Ensemble) is proposed, which generates label-specific features by considering label correlations via clustering ensemble techniques. Extensive experimental results show that, LIFTACE can achieve better generalization performance than LIFT by exploiting label correlations in label-specific features generation.

In multi-label learning, each training example is associated with multiple class labels and the task is to learn a mapping from the feature space to the power set of label space. It is generally demanding and time-consuming to obtain labels for training examples, especially for multi-label learning task where a number of class labels need to be annotated for the instance. To circumvent this difficulty, semi-supervised multi-label learning aims to exploit the readily-available unlabeled data to help build multi-label predictive model. Nonetheless, most semi-supervised solutions to multi-label learning work under transductive setting, which only focus on making predictions on existing unlabeled data and cannot generalize to unseen instances. In this paper, a novel approach named COINS is proposed to learning from labeled and unlabeled data by adapting the well-known co-training strategy which naturally works under inductive setting. In each co-training round, a dichotomy over the feature space is learned by maximizing the diversity between the two classifiers induced on either dichotomized feature subset. After that, pairwise ranking predictions on unlabeled data are communicated between either classifier for model refinement. Extensive experiments on a number of benchmark data sets show that COINS performs favorably against state-of-the-art multi-label learning approaches.

In partial label learning, each training example is associated with a set of candidate labels among which only one is the ground-truth label. The common strategy to induce predictive model is trying to disambiguate the candidate label set, i.e., differentiating the modeling outputs of individual candidate labels. Specifically, disambiguation by differentiation can be conducted either by identifying the ground-truth label iteratively or by treating each candidate label equally. Nonetheless, the disambiguation strategy is prone to be misled by the false positive labels co-occurring with ground-truth label. In this paper, a new partial label learning strategy is studied which refrains from conducting disambiguation. Specifically, by adapting error-correcting output codes (ECOC), a simple yet effective approach named PL-ECOC is proposed by utilizing candidate label set as an entirety. During training phase, to build binary classifier w.r.t. each column coding, any partially labeled example will be regarded as a positive or negative training example only if its candidate label set entirely falls into the coding dichotomy. During testing phase, class label for the unseen instance is determined via loss-based decoding which considers binary classifiers' empirical performance and predictive margin. Extensive experiments show that PL-ECOC performs favorably against state-of-the-art partial label learning approaches.

Partial label learning deals with the problem where each training example is represented by a feature vector while associated with a set of candidate labels, among which only one label is valid. To learn from such ambiguous labeling information, the key is to try to disambiguate the candidate label sets of partial label training examples. Existing disambiguation strategies work by either identifying the ground-truth label iteratively or treating each candidate label equally. Nonetheless, the disambiguation process is generally conducted by focusing on manipulating the label space, and thus ignores making full use of potentially useful information from the feature space. In this paper, a novel two-stage approach is proposed to learning from partial label examples based on feature-aware disambiguation. In the first stage, the manifold structure of feature space is utilized to generate normalized labeling confidences over candidate label set. In the second stage, the predictive model is learned by performing regularized multi-output regression over the generated labeling confidences. Extensive experiments on artificial as well as real-world partial label data sets clearly validate the superiority of the proposed feature-aware disambiguation approach.

In multi-label learning, each training example is represented by a single instance while associated with multiple labels, and the task is to predict a set of relevant labels for the unseen instance. Existing approaches learn from multi-label data by assuming equal labeling-importance, i.e. all the associated labels are regarded to be relevant while their relative importance for the training example are not differentiated. Nonetheless, this assumption fails to reflect the fact that the importance degree of each associated label is generally different, though the importance information is not explicitly accessible from the training examples. In this paper, we show that effective multi-label learning can be achieved by leveraging the implicit relative labeling-importance (RLI) information. Specifically, RLI degrees are formalized as multinomial distribution over the label space, which are estimated by adapting an iterative label propagation procedure. After that, the multi-label prediction model is learned by fitting the estimated multinomial distribution as regularized with popular multi-label empirical loss. Comprehensive experiments clearly validate the usefulness of leveraging implicit RLI information to learn from multi-label data.

Partial label learning deals with the problem where each training example is associated with a set of candidate labels, among which only one is correct. The common strategy is to try to disambiguate their candidate labels, such as by identifying the ground-truth label iteratively or by treating each candidate label equally. Nevertheless, the above disambiguation strategy is prone to be misled by the false positive label(s) within candidate label set. In this paper, a new disambiguation-free approach to partial label learning is proposed by employing the well-known error-correcting output codes (ECOC) techniques. Specifically, to build the binary classifier with respect to each column coding, any partially labeled example will be regarded as a positive or negative training example only if its candidate label set entirely falls into the coding dichotomy. Experiments on controlled and real-world data sets clearly validate the effectiveness of the proposed approach.

Multi-label learning studies the problem where each example is represented by a single instance while associated with a set of labels simultaneously. During the past decade, significant amount of progresses have been made toward this emerging machine learning paradigm. This paper aims to provide a timely review on this area with emphasis on state-of-the-art multi-label learning algorithms. Firstly, fundamentals on multi-label learning including formal definition and evaluation metrics are given. Secondly and primarily, eight representative multi-label learning algorithms are scrutinized under common notations with relevant analyses and discussions. Thirdly, several related learning settings are briefly summarized. As a conclusion, online resources and open research problems on multi-label learning are outlined for reference purposes.

Ensemble learning aims to improve generalization ability by using multiple base learners. It is well-known that to construct a good ensemble, the base learners should be accurate as well as diverse. In this paper, unlabeled data is exploited to facilitate ensemble learning by helping augment the diversity among the base learners. Specifically, a semi-supervised ensemble method named UDEED is proposed. Unlike existing semi-supervised ensemble methods where error-prone pseudo-labels are estimated for unlabeled data to enlarge the labeled data to improve accuracy, UDEED works by maximizing accuracies of base learners on labeled data while maximizing diversity among them on unlabeled data. Experiments show that UDEED can effectively utilize unlabeled data for ensemble learning and is highly competitive to well-established semi-supervised ensemble methods.

In multi-label learning, each training example is associated with a set of labels and the task is to predict the proper label set for the unseen example. Due to the tremendous (exponential) number of possible label sets, the task of learning from multi-label examples is rather challenging. Therefore, the key to successful multi-label learning is how to effectively exploit correlations between different labels to facilitate the learning process. In this paper, we propose to use a Bayesian network structure to efficiently encode the conditional dependencies of the labels as well as the feature set, with the feature set as the common parent of all labels. To make it practical, we give an approximate yet efficient procedure to find such a network structure. With the help of this network, multi-label learning is decomposed into a series of single-label classification problems, where a classifier is constructed for each label by incorporating its parental labels as additional features. Label sets of unseen examples are predicted recursively according to the label ordering given by the network. Extensive experiments on a broad range of data sets validate the effectiveness of our approach against other well-established methods.

Multi-instance multi-label learning (MIML) deals with the problem where each training example is associated with not only multiple instances but also multiple class labels. Previous MIML algorithms work by identifying its equivalence in degenerated versions of multi-instance multi-label learning. However, useful information encoded in training examples may get lost during the identification process. In this paper, a maximum margin method is proposed for MIML which directly exploits the connections between instances and labels. The learning task is formulated as a quadratic programming (QP) problem and implemented in its dual form. Applications to scene classification and text categorization show that the proposed approach achieves superior performance over existing MIML methods.

In multilabel learning, each instance in the training set is associated with a set of labels and the task is to output a label set whose size is unknown a priori for each unseen instance. In this paper, this problem is addressed in the way that a neural network algorithm named BP-MLL, i.e., Backpropagation for Multilabel Learning, is proposed. It is derived from the popular Backpropogation algorithm through employing a novel error function capturing the characteristics of multilabel learning, i.e., the labels belonging to an instance should be ranked higher than those not belonging to that instance. Applications to two real-world multilabel learning problems, i.e., functional genomics and text categorization, show that the performance of BP-MLL is superior to that of some well-established multilabel learning algorithms.