Label Distribution

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Xin Geng - One of the best experts on this subject based on the ideXlab platform.

  • Label Enhancement for Label Distribution Learning
    IEEE Transactions on Knowledge and Data Engineering, 2021
    Co-Authors: Yun-peng Liu, Xin Geng
    Abstract:

    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.

  • A Novel Probabilistic Label Enhancement Algorithm for Multi-Label Distribution Learning
    IEEE Transactions on Knowledge and Data Engineering, 2021
    Co-Authors: Chao Tan, Sheng Chen, Xin Geng
    Abstract:

    We propose a novel probabilistic Label enhancement algorithm, called PLEA, to solve challenging Label Distribution learning (LDL) for multi-Label classification problems. We adopt the well-known maximum entropy model based Label Distribution learner. However, unlike the existing LDL algorithms based on the maximum entropy model, we propose to use manifold learning to enhance the Label Distribution learner. Specifically, the supervised information in the Label manifold is utilized in the feature manifold space construction to improve the accuracy of feature extraction, while dramatically reducing the feature dimension. Then the robust linear regression is employed to estimate the Label Distributions associated with the extracted reduced-dimension features. Using the enhanced reduced-dimension features and their associated estimated Label Distributions in the maximum entropy model, the unknown true Label Distributions can be estimated more accurately, while imposing considerably lower computational complexity. We evaluate the proposed PLEA method on a wide-range artificial and high-dimensional real-world datasets. Experimental results obtained demonstrate that our proposed PLEA method has advantages in LDL accuracy and runtime performance, compared to the latest multi-Label LDL approaches. The results also show that our PLEA compares favourably with the state-of-the-arts multi-Label learning algorithms for classification tasks.

  • Practical age estimation using deep Label Distribution learning
    Frontiers of Computer Science, 2020
    Co-Authors: Huiying Zhang, Yu Zhang, Xin Geng
    Abstract:

    Age estimation plays an important role in humancomputer interaction system. The lack of large number of facial images with definite age Label makes age estimation algorithms inefficient. Deep Label Distribution learning (DLDL) which employs convolutional neural networks (CNN) and Label Distribution learning to learn ambiguity from ground-truth age and adjacent ages, has been proven to outperform current state-of-the-art framework. However, DLDL assumes a rough Label Distribution which covers all ages for any given age Label. In this paper, a more practical Label Distribution paradigm is proposed: we limit age Label Distribution that only covers a reasonable number of neighboring ages. In addition, we explore different Label Distributions to improve the performance of the proposed learning model. We employ CNN and the improved Label Distribution learning to estimate age. Experimental results show that compared to the DLDL, our method is more effective for facial age recognition.

  • Label Distribution learning on auxiliary Label space graphs for facial expression recognition
    Computer Vision and Pattern Recognition, 2020
    Co-Authors: Shikai Chen, Xin Geng, Jianfeng Wang, Yuedong Chen, Zhongchao Shi, Yong Rui
    Abstract:

    Many existing studies reveal that annotation inconsistency widely exists among a variety of facial expression recognition (FER) datasets. The reason might be the subjectivity of human annotators and the ambiguous nature of the expression Labels. One promising strategy tackling such a problem is a recently proposed learning paradigm called Label Distribution Learning (LDL), which allows multiple Labels with different intensity to be linked to one expression. However, it is often impractical to directly apply Label Distribution learning because numerous existing datasets only contain one-hot Labels rather than Label Distributions. To solve the problem, we propose a novel approach named Label Distribution Learning on Auxiliary Label Space Graphs(LDL-ALSG) that leverages the topological information of the Labels from related but more distinct tasks, such as action unit recognition and facial landmark detection. The underlying assumption is that facial images should have similar expression Distributions to their neighbours in the Label space of action unit recognition and facial landmark detection. Our proposed method is evaluated on a variety of datasets and outperforms those state-of-the-art methods consistently with a huge margin.

  • Partial Multi-Label Learning with Label Distribution
    Proceedings of the AAAI Conference on Artificial Intelligence, 2020
    Co-Authors: Yun-peng Liu, Xin Geng
    Abstract:

    Partial multi-Label learning (PML) aims to learn from training examples each associated with a set of candidate Labels, among which only a subset are valid for the training example. The common strategy to induce predictive model is trying to disambiguate the candidate Label set, such as identifying the ground-truth Label via utilizing the confidence of each candidate Label or estimating the noisy Labels in the candidate Label sets. Nonetheless, these strategies ignore considering the essential Label Distribution corresponding to each instance since the Label Distribution is not explicitly available in the training set. In this paper, a new partial multi-Label learning strategy named Pml-ld is proposed to learn from partial multi-Label examples via Label enhancement. Specifically, Label Distributions are recovered by leveraging the topological information of the feature space and the correlations among the Labels. After that, a multi-class predictive model is learned by fitting a regularized multi-output regressor with the recovered Label Distributions. Experimental results on synthetic as well as real-world datasets clearly validate the effectiveness of Pml-ld for solving PML problems.

Yun-peng Liu - One of the best experts on this subject based on the ideXlab platform.

  • Label Enhancement for Label Distribution Learning
    IEEE Transactions on Knowledge and Data Engineering, 2021
    Co-Authors: Yun-peng Liu, Xin Geng
    Abstract:

    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.

  • Partial Multi-Label Learning with Label Distribution
    Proceedings of the AAAI Conference on Artificial Intelligence, 2020
    Co-Authors: Yun-peng Liu, Xin Geng
    Abstract:

    Partial multi-Label learning (PML) aims to learn from training examples each associated with a set of candidate Labels, among which only a subset are valid for the training example. The common strategy to induce predictive model is trying to disambiguate the candidate Label set, such as identifying the ground-truth Label via utilizing the confidence of each candidate Label or estimating the noisy Labels in the candidate Label sets. Nonetheless, these strategies ignore considering the essential Label Distribution corresponding to each instance since the Label Distribution is not explicitly available in the training set. In this paper, a new partial multi-Label learning strategy named Pml-ld is proposed to learn from partial multi-Label examples via Label enhancement. Specifically, Label Distributions are recovered by leveraging the topological information of the feature space and the correlations among the Labels. After that, a multi-class predictive model is learned by fitting a regularized multi-output regressor with the recovered Label Distributions. Experimental results on synthetic as well as real-world datasets clearly validate the effectiveness of Pml-ld for solving PML problems.

  • AAAI - Partial Multi-Label Learning with Label Distribution
    2020
    Co-Authors: Yun-peng Liu, Xin Geng
    Abstract:

    Partial multi-Label learning (PML) aims to learn from training examples each associated with a set of candidate Labels, among which only a subset are valid for the training example. The common strategy to induce predictive model is trying to disambiguate the candidate Label set, such as identifying the ground-truth Label via utilizing the confidence of each candidate Label or estimating the noisy Labels in the candidate Label sets. Nonetheless, these strategies ignore considering the essential Label Distribution corresponding to each instance since the Label Distribution is not explicitly available in the training set. In this paper, a new partial multi-Label learning strategy named Pml-ld is proposed to learn from partial multi-Label examples via Label enhancement. Specifically, Label Distributions are recovered by leveraging the topological information of the feature space and the correlations among the Labels. After that, a multi-class predictive model is learned by fitting a regularized multi-output regressor with the recovered Label Distributions. Experimental results on synthetic as well as real-world datasets clearly validate the effectiveness of Pml-ld for solving PML problems.

  • IJCAI - Label Distribution for Learning with Noisy Labels
    Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence, 2020
    Co-Authors: Yun-peng Liu, Yu Zhang, Xin Geng
    Abstract:

    The performances of deep neural networks (DNNs) crucially rely on the quality of Labeling. In some situations, Labels are easily corrupted, and therefore some Labels become noisy Labels. Thus, designing algorithms that deal with noisy Labels is of great importance for learning robust DNNs. However, it is difficult to distinguish between clean Labels and noisy Labels, which becomes the bottleneck of many methods. To address the problem, this paper proposes a novel method named Label Distribution based Confidence Estimation (LDCE). LDCE estimates the confidence of the observed Labels based on Label Distribution. Then, the boundary between clean Labels and noisy Labels becomes clear according to confidence scores. To verify the effectiveness of the method, LDCE is combined with the existing learning algorithm to train robust DNNs. Experiments on both synthetic and real-world datasets substantiate the superiority of the proposed algorithm against state-of-the-art methods.

Yu Zhang - One of the best experts on this subject based on the ideXlab platform.

  • Practical age estimation using deep Label Distribution learning
    Frontiers of Computer Science, 2020
    Co-Authors: Huiying Zhang, Yu Zhang, Xin Geng
    Abstract:

    Age estimation plays an important role in humancomputer interaction system. The lack of large number of facial images with definite age Label makes age estimation algorithms inefficient. Deep Label Distribution learning (DLDL) which employs convolutional neural networks (CNN) and Label Distribution learning to learn ambiguity from ground-truth age and adjacent ages, has been proven to outperform current state-of-the-art framework. However, DLDL assumes a rough Label Distribution which covers all ages for any given age Label. In this paper, a more practical Label Distribution paradigm is proposed: we limit age Label Distribution that only covers a reasonable number of neighboring ages. In addition, we explore different Label Distributions to improve the performance of the proposed learning model. We employ CNN and the improved Label Distribution learning to estimate age. Experimental results show that compared to the DLDL, our method is more effective for facial age recognition.

  • Head Pose Estimation Based on Multivariate Label Distribution.
    IEEE Transactions on Pattern Analysis and Machine Intelligence, 2020
    Co-Authors: Xin Geng, Xin Qian, Zengwei Huo, Yu Zhang
    Abstract:

    Accurate ground-truth pose is essential to the training of most existing head pose estimation methods. However, in many cases, the "ground truth" pose is obtained in rather subjective ways, such as asking the subjects to stare at different markers on the wall. Thus it is better to use soft Labels rather than explicit hard Labels to indicate the pose of a face image. This paper proposes to associate a Multivariate Label Distribution (MLD) to each image. An MLD covers a neighborhood around the original pose. Labeling the images with MLD can not only alleviate the problem of inaccurate pose Labels, but also boost the training examples associated to each pose without actually increasing the total amount of training examples. Four algorithms are proposed to learn from MLD. Furthermore, an extension of MLD with the hierarchical structure is proposed to deal with fine-grained head pose estimation, which is named Hierarchical Multivariate Label Distribution (HMLD). Experimental results show that the MLD-based methods perform significantly better than the compared state-of-the-art head pose estimation algorithms. Moreover, the MLD-based methods appear much more robust against the Label noise in the training set than the compared baseline methods.

  • IJCAI - Label Distribution for Learning with Noisy Labels
    Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence, 2020
    Co-Authors: Yun-peng Liu, Yu Zhang, Xin Geng
    Abstract:

    The performances of deep neural networks (DNNs) crucially rely on the quality of Labeling. In some situations, Labels are easily corrupted, and therefore some Labels become noisy Labels. Thus, designing algorithms that deal with noisy Labels is of great importance for learning robust DNNs. However, it is difficult to distinguish between clean Labels and noisy Labels, which becomes the bottleneck of many methods. To address the problem, this paper proposes a novel method named Label Distribution based Confidence Estimation (LDCE). LDCE estimates the confidence of the observed Labels based on Label Distribution. Then, the boundary between clean Labels and noisy Labels becomes clear according to confidence scores. To verify the effectiveness of the method, LDCE is combined with the existing learning algorithm to train robust DNNs. Experiments on both synthetic and real-world datasets substantiate the superiority of the proposed algorithm against state-of-the-art methods.

  • IJCAI - Label Enhancement for Label Distribution Learning via Prior Knowledge.
    Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence, 2020
    Co-Authors: Yongbiao Gao, Yu Zhang, Xin Geng
    Abstract:

    Label Distribution learning (LDL) is a novel machine learning paradigm that gives a description degree of each Label to an instance. However, most of training datasets only contain simple logical Labels rather than Label Distributions due to the difficulty of obtaining the Label Distributions directly. We propose to use the prior knowledge to recover the Label Distributions. The process of recovering the Label Distributions from the logical Labels is called Label enhancement. In this paper, we formulate the Label enhancement as a dynamic decision process. Thus, the Label Distribution is adjusted by a series of actions conducted by a reinforcement learning agent according to sequential state representations. The target state is defined by the prior knowledge. Experimental results show that the proposed approach outperforms the state-of-the-art methods in both age estimation and image emotion recognition.

Wenbin Qian - One of the best experts on this subject based on the ideXlab platform.

  • Label Distribution feature selection for multi-Label classification with rough set
    International Journal of Approximate Reasoning, 2021
    Co-Authors: Wenbin Qian, Jintao Huang, Yinglong Wang, Yonghong Xie
    Abstract:

    Abstract Multi-Label learning deals with cases where every instance corresponds to multiple Labels. The objective is to learn mapping from an instance to a relevant Label set. Existing multi-Label learning approaches assume that the significance for all related Labels is same for every instance. Several problems of Label ambiguity can be dealt with using multi-Label learning, but some practical applications with significance among related Labels for every instance cannot be effectively processed. To achieve superior results by conducting different significance of Labels, Label Distribution learning is used for such applications. First, the probability model and rough set are embedded in the Labeling significance, thus more supervised information can be obtained from original multi-Label data. Subsequently, to resolve the feature selection problem of Label Distribution data, according to the feature dependency and the rough set, a novel feature selection algorithm for multi-Label classification is designed. Finally, to verify the effectiveness of the proposed algorithms, an extensive experiment is conducted on 15 real-world multiple Label data sets. The performance of the proposed algorithm through the multi-Label classifier is compared with seven state-of-the-art approaches, thereby indicating the applicability and effectiveness of Label Distribution feature selection.

  • Mutual information-based Label Distribution feature selection for multi-Label learning
    Knowledge-Based Systems, 2020
    Co-Authors: Wenbin Qian, Jintao Huang, Yinglong Wang, Wenhao Shu
    Abstract:

    Abstract Feature selection used for dimensionality reduction of the feature space plays an important role in multi-Label learning where high-dimensional data are involved. Although most existing multi-Label feature selection approaches can deal with the problem of Label ambiguity which mainly focuses on the assumption of uniform Distribution with logical Labels, it cannot be applied to many practical applications where the significance of related Label for every instance tends to be different. To deal with this issue, in this study, Label Distribution learning covered with a certain real number of Labels is introduced to design a model for the Labeling-significance. Nevertheless, multi-Label feature selection is limited to handling only Labels consisting of logical relations. In order to solve this problem, combining the random variable Distribution with granular computing, we first propose a Label enhancement algorithm to transform logical Labels in multi-Label data into Label Distribution with more supervised information, which can mine the hidden Label significance from every instance. On this basis, to remove some redundant or irrelevant features in multi-Label data, a Label Distribution feature selection algorithm using mutual information and Label enhancement is developed. Finally, the experimental results show that the performance of the proposed method is superior to the other state-of-the-art approaches when dealing with multi-Label data.

  • Multi-Label feature selection based on Label Distribution and feature complementarity
    Applied Soft Computing, 2020
    Co-Authors: Wenbin Qian, Yinglong Wang, Xuandong Long, Yonghong Xie
    Abstract:

    Abstract In the real-world, data in various domains usually tend to be high-dimensional, which may result in considerable time complexity and poor performance for multi-Label classification problems. Multi-Label feature selection is an important preprocessing step in machine learning, which can effectively solve the so-called “curse of dimensionality” by removing irrelevant and redundant features. Nevertheless, the significance of related Labels for each instance is generally different, which is an issue that most of the existing multi-Label feature selection algorithms have not addressed. Hence, in this paper, we integrate Label-Distribution learning into multi-Label feature selection from the perspective of granular computing with considering multiple feature correlations. Then, a novel multi-Label feature selection algorithm based on Label Distribution and feature complementarity is developed. In addition, the proposed algorithm consists of two primary parts: first, the different significances of related Labels for each instance in the multi-Label data are obtained based on granular computing; second, the feature complementarity is estimated based on neighborhood mutual information without discretization. Moreover, the superiority of our proposed method over other state-of-the-art methods is demonstrated by conducting comprehensive experiments with 10 publicly available multi-Label datasets on six widely-used metrics. Finally, the proposed method can significantly improve the performance of the classifier while reducing the dimension of the original data.

Yonghong Xie - One of the best experts on this subject based on the ideXlab platform.

  • Label Distribution feature selection for multi-Label classification with rough set
    International Journal of Approximate Reasoning, 2021
    Co-Authors: Wenbin Qian, Jintao Huang, Yinglong Wang, Yonghong Xie
    Abstract:

    Abstract Multi-Label learning deals with cases where every instance corresponds to multiple Labels. The objective is to learn mapping from an instance to a relevant Label set. Existing multi-Label learning approaches assume that the significance for all related Labels is same for every instance. Several problems of Label ambiguity can be dealt with using multi-Label learning, but some practical applications with significance among related Labels for every instance cannot be effectively processed. To achieve superior results by conducting different significance of Labels, Label Distribution learning is used for such applications. First, the probability model and rough set are embedded in the Labeling significance, thus more supervised information can be obtained from original multi-Label data. Subsequently, to resolve the feature selection problem of Label Distribution data, according to the feature dependency and the rough set, a novel feature selection algorithm for multi-Label classification is designed. Finally, to verify the effectiveness of the proposed algorithms, an extensive experiment is conducted on 15 real-world multiple Label data sets. The performance of the proposed algorithm through the multi-Label classifier is compared with seven state-of-the-art approaches, thereby indicating the applicability and effectiveness of Label Distribution feature selection.

  • Multi-Label feature selection based on Label Distribution and feature complementarity
    Applied Soft Computing, 2020
    Co-Authors: Wenbin Qian, Yinglong Wang, Xuandong Long, Yonghong Xie
    Abstract:

    Abstract In the real-world, data in various domains usually tend to be high-dimensional, which may result in considerable time complexity and poor performance for multi-Label classification problems. Multi-Label feature selection is an important preprocessing step in machine learning, which can effectively solve the so-called “curse of dimensionality” by removing irrelevant and redundant features. Nevertheless, the significance of related Labels for each instance is generally different, which is an issue that most of the existing multi-Label feature selection algorithms have not addressed. Hence, in this paper, we integrate Label-Distribution learning into multi-Label feature selection from the perspective of granular computing with considering multiple feature correlations. Then, a novel multi-Label feature selection algorithm based on Label Distribution and feature complementarity is developed. In addition, the proposed algorithm consists of two primary parts: first, the different significances of related Labels for each instance in the multi-Label data are obtained based on granular computing; second, the feature complementarity is estimated based on neighborhood mutual information without discretization. Moreover, the superiority of our proposed method over other state-of-the-art methods is demonstrated by conducting comprehensive experiments with 10 publicly available multi-Label datasets on six widely-used metrics. Finally, the proposed method can significantly improve the performance of the classifier while reducing the dimension of the original data.