The Experts below are selected from a list of 54 Experts worldwide ranked by ideXlab platform

Yulong Wang - One of the best experts on this subject based on the ideXlab platform.

  • modal regression based Atomic Representation for robust face recognition and reconstruction
    IEEE Transactions on Systems Man and Cybernetics, 2020
    Co-Authors: Yulong Wang, Yuan Yan Tang, Hong Chen
    Abstract:

    Representation-based classification (RC) methods, such as sparse RC, have shown great potential in face recognition (FR) in recent years. Most previous RC methods are based on the conventional regression models, such as lasso regression, ridge regression, or group lasso regression. These regression models essentially impose a predefined assumption on the distribution of the noise variable in the query sample, such as the Gaussian or Laplacian distribution. However, the complicated noises in practice may violate the assumptions and impede the performance of these RC methods. In this paper, we propose a modal regression (MR)-based Atomic Representation and classification (MRARC) framework to alleviate such limitations. MR is a robust regression framework which aims to reveal the relationship between the input and response variables by regressing toward the conditional mode function. Atomic Representation is a general Atomic norm regularized linear Representation framework which includes many popular Representation methods, such as sparse Representation, collaborative Representation, and low-rank Representation as special cases. Unlike previous RC methods, the MRARC framework does not require the noise variable to follow any specific predefined distributions. This gives rise to the capability of MRARC in handling various complex noises in reality. Using MRARC as a general platform, we also develop four novel RC methods for unimodal and multimodal FR, respectively. In addition, we devise a general optimization algorithm for the unified MRARC framework based on the alternating direction method of multipliers and half-quadratic theory. The experiments on real-world data validate the efficacy of MRARC for robust FR and reconstruction.

  • Atomic Representation-Based Classification: Theory, Algorithm, and Applications
    IEEE transactions on pattern analysis and machine intelligence, 2017
    Co-Authors: Yulong Wang, Yuan Yan Tang, Hong Chen, Jianjia Pan
    Abstract:

    Representation-based classification (RC) methods such as sparse RC (SRC) have attracted great interest in pattern recognition recently. Despite their empirical success, few theoretical results are reported to justify their effectiveness. In this paper, we establish the theoretical guarantees for a general unified framework termed as Atomic Representation-based classification (ARC), which includes most RC methods as special cases. We introduce a new condition called Atomic classification condition (ACC), which reveals important geometric insights for the theory of ARC. We show that under such condition ARC is provably effective in correctly recognizing any new test sample, even corrupted with noise. Our theoretical analysis significantly broadens the range of conditions under which RC methods succeed for classification in the following two aspects: (1) prior theoretical advances of RC are mainly concerned with the single SRC method while our theory can apply to the general unified ARC framework, including SRC and many other RC methods; and (2) previous works are confined to the analysis of noiseless test data while we provide theoretical guarantees for ARC using both noiseless and noisy test data. Numerical results are provided to validate and complement our theoretical analysis of ARC and its important special cases for both noiseless and noisy test data.

  • modal regression based Atomic Representation for robust face recognition
    arXiv: Computer Vision and Pattern Recognition, 2017
    Co-Authors: Yulong Wang, Yuan Yan Tang, Hong Chen
    Abstract:

    Representation based classification (RC) methods such as sparse RC (SRC) have shown great potential in face recognition in recent years. Most previous RC methods are based on the conventional regression models, such as lasso regression, ridge regression or group lasso regression. These regression models essentially impose a predefined assumption on the distribution of the noise variable in the query sample, such as the Gaussian or Laplacian distribution. However, the complicated noises in practice may violate the assumptions and impede the performance of these RC methods. In this paper, we propose a modal regression based Atomic Representation and classification (MRARC) framework to alleviate such limitation. Unlike previous RC methods, the MRARC framework does not require the noise variable to follow any specific predefined distributions. This gives rise to the capability of MRARC in handling various complex noises in reality. Using MRARC as a general platform, we also develop four novel RC methods for unimodal and multimodal face recognition, respectively. In addition, we devise a general optimization algorithm for the unified MRARC framework based on the alternating direction method of multipliers (ADMM) and half-quadratic theory. The experiments on real-world data validate the efficacy of MRARC for robust face recognition.

  • information theoretic Atomic Representation for robust pattern classification
    International Conference on Pattern Recognition, 2016
    Co-Authors: Yulong Wang, Yuan Yan Tang, Patrick S P Wang
    Abstract:

    Representation-based classifiers (RCs) including sparse RC (SRC) have attracted intensive interest in pattern recognition in recent years. In our previous work, we have proposed a general framework called Atomic Representation-based classifier (ARC) including many popular RCs as special cases. Despite the empirical success, ARC and conventional RCs utilize the mean square error (MSE) criterion and assign the same weights to all entries of the test data, including both severely corrupted and clean ones. This makes ARC sensitive to the entries with large noise and outliers. In this work, we propose an information-theoretic ARC (ITARC) framework to alleviate such limitation of ARC. Using ITARC as a general platform, we develop three novel Representation-based classifiers. The experiments on public real-world datasets demonstrate the efficacy of ITARC for robust pattern recognition.

  • classification via Atomic Representation
    2015 IEEE 2nd International Conference on Cybernetics (CYBCONF), 2015
    Co-Authors: Yulong Wang, Yuan Yan Tang, Lina Yang, Huiwu Luo, Haoliang Yuan, Xianwei Zheng, Jianjia Pan
    Abstract:

    This paper provides a novel and unified framework of Representation based classification technique. The proposed Atomic Representation based classification (ARC) framework includes, but not limited to, sparse Representation based classification (SRC), low-rank Representation based classification (LRRC) as special cases. Despite good performance, most existing classification methods are heavily reliant on the assumption that the training set should cover all possible classes appeared in the test set. Thus, they may fail when there exists a new class that is not observed in the training set. To remedy this drawback, the ARC methods are extended to tackle more practical and tougher situation, in which training samples from only K − 1 classes are available to classify test samples from K classes. The experimental results using simulated and real data sets demonstrate the effectiveness of the proposed method.

Yuan Yan Tang - One of the best experts on this subject based on the ideXlab platform.

  • modal regression based Atomic Representation for robust face recognition and reconstruction
    IEEE Transactions on Systems Man and Cybernetics, 2020
    Co-Authors: Yulong Wang, Yuan Yan Tang, Hong Chen
    Abstract:

    Representation-based classification (RC) methods, such as sparse RC, have shown great potential in face recognition (FR) in recent years. Most previous RC methods are based on the conventional regression models, such as lasso regression, ridge regression, or group lasso regression. These regression models essentially impose a predefined assumption on the distribution of the noise variable in the query sample, such as the Gaussian or Laplacian distribution. However, the complicated noises in practice may violate the assumptions and impede the performance of these RC methods. In this paper, we propose a modal regression (MR)-based Atomic Representation and classification (MRARC) framework to alleviate such limitations. MR is a robust regression framework which aims to reveal the relationship between the input and response variables by regressing toward the conditional mode function. Atomic Representation is a general Atomic norm regularized linear Representation framework which includes many popular Representation methods, such as sparse Representation, collaborative Representation, and low-rank Representation as special cases. Unlike previous RC methods, the MRARC framework does not require the noise variable to follow any specific predefined distributions. This gives rise to the capability of MRARC in handling various complex noises in reality. Using MRARC as a general platform, we also develop four novel RC methods for unimodal and multimodal FR, respectively. In addition, we devise a general optimization algorithm for the unified MRARC framework based on the alternating direction method of multipliers and half-quadratic theory. The experiments on real-world data validate the efficacy of MRARC for robust FR and reconstruction.

  • Atomic Representation-Based Classification: Theory, Algorithm, and Applications
    IEEE transactions on pattern analysis and machine intelligence, 2017
    Co-Authors: Yulong Wang, Yuan Yan Tang, Hong Chen, Jianjia Pan
    Abstract:

    Representation-based classification (RC) methods such as sparse RC (SRC) have attracted great interest in pattern recognition recently. Despite their empirical success, few theoretical results are reported to justify their effectiveness. In this paper, we establish the theoretical guarantees for a general unified framework termed as Atomic Representation-based classification (ARC), which includes most RC methods as special cases. We introduce a new condition called Atomic classification condition (ACC), which reveals important geometric insights for the theory of ARC. We show that under such condition ARC is provably effective in correctly recognizing any new test sample, even corrupted with noise. Our theoretical analysis significantly broadens the range of conditions under which RC methods succeed for classification in the following two aspects: (1) prior theoretical advances of RC are mainly concerned with the single SRC method while our theory can apply to the general unified ARC framework, including SRC and many other RC methods; and (2) previous works are confined to the analysis of noiseless test data while we provide theoretical guarantees for ARC using both noiseless and noisy test data. Numerical results are provided to validate and complement our theoretical analysis of ARC and its important special cases for both noiseless and noisy test data.

  • modal regression based Atomic Representation for robust face recognition
    arXiv: Computer Vision and Pattern Recognition, 2017
    Co-Authors: Yulong Wang, Yuan Yan Tang, Hong Chen
    Abstract:

    Representation based classification (RC) methods such as sparse RC (SRC) have shown great potential in face recognition in recent years. Most previous RC methods are based on the conventional regression models, such as lasso regression, ridge regression or group lasso regression. These regression models essentially impose a predefined assumption on the distribution of the noise variable in the query sample, such as the Gaussian or Laplacian distribution. However, the complicated noises in practice may violate the assumptions and impede the performance of these RC methods. In this paper, we propose a modal regression based Atomic Representation and classification (MRARC) framework to alleviate such limitation. Unlike previous RC methods, the MRARC framework does not require the noise variable to follow any specific predefined distributions. This gives rise to the capability of MRARC in handling various complex noises in reality. Using MRARC as a general platform, we also develop four novel RC methods for unimodal and multimodal face recognition, respectively. In addition, we devise a general optimization algorithm for the unified MRARC framework based on the alternating direction method of multipliers (ADMM) and half-quadratic theory. The experiments on real-world data validate the efficacy of MRARC for robust face recognition.

  • information theoretic Atomic Representation for robust pattern classification
    International Conference on Pattern Recognition, 2016
    Co-Authors: Yulong Wang, Yuan Yan Tang, Patrick S P Wang
    Abstract:

    Representation-based classifiers (RCs) including sparse RC (SRC) have attracted intensive interest in pattern recognition in recent years. In our previous work, we have proposed a general framework called Atomic Representation-based classifier (ARC) including many popular RCs as special cases. Despite the empirical success, ARC and conventional RCs utilize the mean square error (MSE) criterion and assign the same weights to all entries of the test data, including both severely corrupted and clean ones. This makes ARC sensitive to the entries with large noise and outliers. In this work, we propose an information-theoretic ARC (ITARC) framework to alleviate such limitation of ARC. Using ITARC as a general platform, we develop three novel Representation-based classifiers. The experiments on public real-world datasets demonstrate the efficacy of ITARC for robust pattern recognition.

  • classification via Atomic Representation
    2015 IEEE 2nd International Conference on Cybernetics (CYBCONF), 2015
    Co-Authors: Yulong Wang, Yuan Yan Tang, Lina Yang, Huiwu Luo, Haoliang Yuan, Xianwei Zheng, Jianjia Pan
    Abstract:

    This paper provides a novel and unified framework of Representation based classification technique. The proposed Atomic Representation based classification (ARC) framework includes, but not limited to, sparse Representation based classification (SRC), low-rank Representation based classification (LRRC) as special cases. Despite good performance, most existing classification methods are heavily reliant on the assumption that the training set should cover all possible classes appeared in the test set. Thus, they may fail when there exists a new class that is not observed in the training set. To remedy this drawback, the ARC methods are extended to tackle more practical and tougher situation, in which training samples from only K − 1 classes are available to classify test samples from K classes. The experimental results using simulated and real data sets demonstrate the effectiveness of the proposed method.

Hong Chen - One of the best experts on this subject based on the ideXlab platform.

  • modal regression based Atomic Representation for robust face recognition and reconstruction
    IEEE Transactions on Systems Man and Cybernetics, 2020
    Co-Authors: Yulong Wang, Yuan Yan Tang, Hong Chen
    Abstract:

    Representation-based classification (RC) methods, such as sparse RC, have shown great potential in face recognition (FR) in recent years. Most previous RC methods are based on the conventional regression models, such as lasso regression, ridge regression, or group lasso regression. These regression models essentially impose a predefined assumption on the distribution of the noise variable in the query sample, such as the Gaussian or Laplacian distribution. However, the complicated noises in practice may violate the assumptions and impede the performance of these RC methods. In this paper, we propose a modal regression (MR)-based Atomic Representation and classification (MRARC) framework to alleviate such limitations. MR is a robust regression framework which aims to reveal the relationship between the input and response variables by regressing toward the conditional mode function. Atomic Representation is a general Atomic norm regularized linear Representation framework which includes many popular Representation methods, such as sparse Representation, collaborative Representation, and low-rank Representation as special cases. Unlike previous RC methods, the MRARC framework does not require the noise variable to follow any specific predefined distributions. This gives rise to the capability of MRARC in handling various complex noises in reality. Using MRARC as a general platform, we also develop four novel RC methods for unimodal and multimodal FR, respectively. In addition, we devise a general optimization algorithm for the unified MRARC framework based on the alternating direction method of multipliers and half-quadratic theory. The experiments on real-world data validate the efficacy of MRARC for robust FR and reconstruction.

  • Atomic Representation-Based Classification: Theory, Algorithm, and Applications
    IEEE transactions on pattern analysis and machine intelligence, 2017
    Co-Authors: Yulong Wang, Yuan Yan Tang, Hong Chen, Jianjia Pan
    Abstract:

    Representation-based classification (RC) methods such as sparse RC (SRC) have attracted great interest in pattern recognition recently. Despite their empirical success, few theoretical results are reported to justify their effectiveness. In this paper, we establish the theoretical guarantees for a general unified framework termed as Atomic Representation-based classification (ARC), which includes most RC methods as special cases. We introduce a new condition called Atomic classification condition (ACC), which reveals important geometric insights for the theory of ARC. We show that under such condition ARC is provably effective in correctly recognizing any new test sample, even corrupted with noise. Our theoretical analysis significantly broadens the range of conditions under which RC methods succeed for classification in the following two aspects: (1) prior theoretical advances of RC are mainly concerned with the single SRC method while our theory can apply to the general unified ARC framework, including SRC and many other RC methods; and (2) previous works are confined to the analysis of noiseless test data while we provide theoretical guarantees for ARC using both noiseless and noisy test data. Numerical results are provided to validate and complement our theoretical analysis of ARC and its important special cases for both noiseless and noisy test data.

  • modal regression based Atomic Representation for robust face recognition
    arXiv: Computer Vision and Pattern Recognition, 2017
    Co-Authors: Yulong Wang, Yuan Yan Tang, Hong Chen
    Abstract:

    Representation based classification (RC) methods such as sparse RC (SRC) have shown great potential in face recognition in recent years. Most previous RC methods are based on the conventional regression models, such as lasso regression, ridge regression or group lasso regression. These regression models essentially impose a predefined assumption on the distribution of the noise variable in the query sample, such as the Gaussian or Laplacian distribution. However, the complicated noises in practice may violate the assumptions and impede the performance of these RC methods. In this paper, we propose a modal regression based Atomic Representation and classification (MRARC) framework to alleviate such limitation. Unlike previous RC methods, the MRARC framework does not require the noise variable to follow any specific predefined distributions. This gives rise to the capability of MRARC in handling various complex noises in reality. Using MRARC as a general platform, we also develop four novel RC methods for unimodal and multimodal face recognition, respectively. In addition, we devise a general optimization algorithm for the unified MRARC framework based on the alternating direction method of multipliers (ADMM) and half-quadratic theory. The experiments on real-world data validate the efficacy of MRARC for robust face recognition.

Luoqing Li - One of the best experts on this subject based on the ideXlab platform.

  • Robust Face Recognition via Minimum Error Entropy-Based Atomic Representation
    IEEE Transactions on Image Processing, 2015
    Co-Authors: Yulong Wang, Yuan Yan Tang, Luoqing Li
    Abstract:

    Representation-based classifiers (RCs) have attracted considerable attention in face recognition in recent years. However, most existing RCs use the mean square error (MSE) criterion as the cost function, which relies on the Gaussianity assumption of the error distribution and is sensitive to non-Gaussian noise. This may severely degrade the performance of MSE-based RCs in recognizing facial images with random occlusion and corruption. In this paper, we present a minimum error entropy-based Atomic Representation (MEEAR) framework for face recognition. Unlike existing MSE-based RCs, our framework is based on the minimum error entropy criterion, which is not dependent on the error distribution and shown to be more robust to noise. In particular, MEEAR can produce discriminative Representation vector by minimizing the Atomic norm regularized Renyi's entropy of the reconstruction error. The optimality conditions are provided for general Atomic Representation model. As a general framework, MEEAR can also be used as a platform to develop new classifiers. Two effective MEE-based RCs are proposed by defining appropriate Atomic sets. The experimental results on popular face databases show that MEEAR can improve both the recognition accuracy and the reconstructed results compared with the state-of-the-art MSE-based RCs.

Jianjia Pan - One of the best experts on this subject based on the ideXlab platform.

  • Atomic Representation-Based Classification: Theory, Algorithm, and Applications
    IEEE transactions on pattern analysis and machine intelligence, 2017
    Co-Authors: Yulong Wang, Yuan Yan Tang, Hong Chen, Jianjia Pan
    Abstract:

    Representation-based classification (RC) methods such as sparse RC (SRC) have attracted great interest in pattern recognition recently. Despite their empirical success, few theoretical results are reported to justify their effectiveness. In this paper, we establish the theoretical guarantees for a general unified framework termed as Atomic Representation-based classification (ARC), which includes most RC methods as special cases. We introduce a new condition called Atomic classification condition (ACC), which reveals important geometric insights for the theory of ARC. We show that under such condition ARC is provably effective in correctly recognizing any new test sample, even corrupted with noise. Our theoretical analysis significantly broadens the range of conditions under which RC methods succeed for classification in the following two aspects: (1) prior theoretical advances of RC are mainly concerned with the single SRC method while our theory can apply to the general unified ARC framework, including SRC and many other RC methods; and (2) previous works are confined to the analysis of noiseless test data while we provide theoretical guarantees for ARC using both noiseless and noisy test data. Numerical results are provided to validate and complement our theoretical analysis of ARC and its important special cases for both noiseless and noisy test data.

  • classification via Atomic Representation
    2015 IEEE 2nd International Conference on Cybernetics (CYBCONF), 2015
    Co-Authors: Yulong Wang, Yuan Yan Tang, Lina Yang, Huiwu Luo, Haoliang Yuan, Xianwei Zheng, Jianjia Pan
    Abstract:

    This paper provides a novel and unified framework of Representation based classification technique. The proposed Atomic Representation based classification (ARC) framework includes, but not limited to, sparse Representation based classification (SRC), low-rank Representation based classification (LRRC) as special cases. Despite good performance, most existing classification methods are heavily reliant on the assumption that the training set should cover all possible classes appeared in the test set. Thus, they may fail when there exists a new class that is not observed in the training set. To remedy this drawback, the ARC methods are extended to tackle more practical and tougher situation, in which training samples from only K − 1 classes are available to classify test samples from K classes. The experimental results using simulated and real data sets demonstrate the effectiveness of the proposed method.