The Experts below are selected from a list of 327 Experts worldwide ranked by ideXlab platform
Yanwen Chong - One of the best experts on this subject based on the ideXlab platform.
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robust spatial spectral block Diagonal Structure representation with fuzzy class probability for hyperspectral image classification
IEEE Transactions on Geoscience and Remote Sensing, 2020Co-Authors: Yun Ding, Shaoming Pan, Yanwen ChongAbstract:Generally, the apparent spectral information of hyperspectral images (HSIs) is directly used to measure the similarity among HSI pixels in the feature space, but this process cannot reveal the inherent characteristics of HSI pixels. Moreover, constructing spatial–spectral block-Diagonal subspace Structure representations of intraclass land-cover samples remains a challenge for low-rank representation (LRR) in HSI classification. In this article, we propose two methods to reveal the complex intrinsic spatial–spectral features of HSIs using block-Diagonal subspace Structures, namely, the spatial–spectral block-Diagonal Structure representation with class probability (SSBDCP) and spatial-spectral block-Diagonal Structure representation with fuzzy class probability (SSBDFCP) methods, for HSI classification. First, the SSBDFCP and SSBDCP methods explore the Structure similarity characteristics of the latent subspace to form a block-Diagonal LRR (BDLRR) of intraclass pixels with class probability and fuzzy class probability (FCP) and suppress the interclass pixels’ representations. Then, the spatial information is considered in the proposed methods to enhance spatial–spectral graph expression and capture more comprehensive information. Note that SSBDFCP can perform better than SSBDCP because the FCP considers the “typicalness” that a sample belongs to a specific category and utilizes complex intrinsic discriminative information based on the feedback of “weakly” supervised information. Moreover, the feedback information can remove the noise features around the pixels and take advantage of the benefits of true neighbors. The experimental results for the Indian Pines and Pavia University data sets show that the SSBDFCP and SSBDCP methods achieve better HSI classification results than other popular graph construction methods.
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Robust Spatial–Spectral Block-Diagonal Structure Representation With Fuzzy Class Probability for Hyperspectral Image Classification
IEEE Transactions on Geoscience and Remote Sensing, 2020Co-Authors: Yun Ding, Shaoming Pan, Yanwen ChongAbstract:Generally, the apparent spectral information of hyperspectral images (HSIs) is directly used to measure the similarity among HSI pixels in the feature space, but this process cannot reveal the inherent characteristics of HSI pixels. Moreover, constructing spatial–spectral block-Diagonal subspace Structure representations of intraclass land-cover samples remains a challenge for low-rank representation (LRR) in HSI classification. In this article, we propose two methods to reveal the complex intrinsic spatial–spectral features of HSIs using block-Diagonal subspace Structures, namely, the spatial–spectral block-Diagonal Structure representation with class probability (SSBDCP) and spatial-spectral block-Diagonal Structure representation with fuzzy class probability (SSBDFCP) methods, for HSI classification. First, the SSBDFCP and SSBDCP methods explore the Structure similarity characteristics of the latent subspace to form a block-Diagonal LRR (BDLRR) of intraclass pixels with class probability and fuzzy class probability (FCP) and suppress the interclass pixels’ representations. Then, the spatial information is considered in the proposed methods to enhance spatial–spectral graph expression and capture more comprehensive information. Note that SSBDFCP can perform better than SSBDCP because the FCP considers the “typicalness” that a sample belongs to a specific category and utilizes complex intrinsic discriminative information based on the feedback of “weakly” supervised information. Moreover, the feedback information can remove the noise features around the pixels and take advantage of the benefits of true neighbors. The experimental results for the Indian Pines and Pavia University data sets show that the SSBDFCP and SSBDCP methods achieve better HSI classification results than other popular graph construction methods.
Yun Ding - One of the best experts on this subject based on the ideXlab platform.
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robust spatial spectral block Diagonal Structure representation with fuzzy class probability for hyperspectral image classification
IEEE Transactions on Geoscience and Remote Sensing, 2020Co-Authors: Yun Ding, Shaoming Pan, Yanwen ChongAbstract:Generally, the apparent spectral information of hyperspectral images (HSIs) is directly used to measure the similarity among HSI pixels in the feature space, but this process cannot reveal the inherent characteristics of HSI pixels. Moreover, constructing spatial–spectral block-Diagonal subspace Structure representations of intraclass land-cover samples remains a challenge for low-rank representation (LRR) in HSI classification. In this article, we propose two methods to reveal the complex intrinsic spatial–spectral features of HSIs using block-Diagonal subspace Structures, namely, the spatial–spectral block-Diagonal Structure representation with class probability (SSBDCP) and spatial-spectral block-Diagonal Structure representation with fuzzy class probability (SSBDFCP) methods, for HSI classification. First, the SSBDFCP and SSBDCP methods explore the Structure similarity characteristics of the latent subspace to form a block-Diagonal LRR (BDLRR) of intraclass pixels with class probability and fuzzy class probability (FCP) and suppress the interclass pixels’ representations. Then, the spatial information is considered in the proposed methods to enhance spatial–spectral graph expression and capture more comprehensive information. Note that SSBDFCP can perform better than SSBDCP because the FCP considers the “typicalness” that a sample belongs to a specific category and utilizes complex intrinsic discriminative information based on the feedback of “weakly” supervised information. Moreover, the feedback information can remove the noise features around the pixels and take advantage of the benefits of true neighbors. The experimental results for the Indian Pines and Pavia University data sets show that the SSBDFCP and SSBDCP methods achieve better HSI classification results than other popular graph construction methods.
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Robust Spatial–Spectral Block-Diagonal Structure Representation With Fuzzy Class Probability for Hyperspectral Image Classification
IEEE Transactions on Geoscience and Remote Sensing, 2020Co-Authors: Yun Ding, Shaoming Pan, Yanwen ChongAbstract:Generally, the apparent spectral information of hyperspectral images (HSIs) is directly used to measure the similarity among HSI pixels in the feature space, but this process cannot reveal the inherent characteristics of HSI pixels. Moreover, constructing spatial–spectral block-Diagonal subspace Structure representations of intraclass land-cover samples remains a challenge for low-rank representation (LRR) in HSI classification. In this article, we propose two methods to reveal the complex intrinsic spatial–spectral features of HSIs using block-Diagonal subspace Structures, namely, the spatial–spectral block-Diagonal Structure representation with class probability (SSBDCP) and spatial-spectral block-Diagonal Structure representation with fuzzy class probability (SSBDFCP) methods, for HSI classification. First, the SSBDFCP and SSBDCP methods explore the Structure similarity characteristics of the latent subspace to form a block-Diagonal LRR (BDLRR) of intraclass pixels with class probability and fuzzy class probability (FCP) and suppress the interclass pixels’ representations. Then, the spatial information is considered in the proposed methods to enhance spatial–spectral graph expression and capture more comprehensive information. Note that SSBDFCP can perform better than SSBDCP because the FCP considers the “typicalness” that a sample belongs to a specific category and utilizes complex intrinsic discriminative information based on the feedback of “weakly” supervised information. Moreover, the feedback information can remove the noise features around the pixels and take advantage of the benefits of true neighbors. The experimental results for the Indian Pines and Pavia University data sets show that the SSBDFCP and SSBDCP methods achieve better HSI classification results than other popular graph construction methods.
Qingming Huang - One of the best experts on this subject based on the ideXlab platform.
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generalized block Diagonal Structure pursuit learning soft latent task assignment against negative transfer
Neural Information Processing Systems, 2019Co-Authors: Zhiyong Yang, Yangbangyan Jiang, Xiaochun Cao, Qingming HuangAbstract:In multi-task learning, a major challenge springs from a notorious issue known as negative transfer, which refers to the phenomenon that sharing the knowledge with dissimilar and hard tasks often results in a worsened performance. To circumvent this issue, we propose a novel multi-task learning method, which simultaneously learns latent task representations and a block-Diagonal Latent Task Assignment Matrix (LTAM). Different from most of the previous work, pursuing the Block-Diagonal Structure of LTAM (assigning latent tasks to output tasks) alleviates negative transfer via collaboratively grouping latent tasks and output tasks such that inter-group knowledge transfer and sharing is suppressed. This goal is challenging, since 1) our notion of Block-Diagonal Property extends the traditional notion for square matrices where the $i$-th column and the $i$-th column represents the same concept; 2) marginal constraints on rows and columns are also required for avoiding isolated latent/output tasks. Facing such challenges, we propose a novel regularizer by means of an equivalent spectral condition realizing this generalized block-Diagonal property. Practically, we provide a relaxation scheme which improves the flexibility of the model. With the objective function given, we then propose an alternating optimization method, which not only tells how negative transfer is alleviated in our method but also reveals an interesting connection between our method and the optimal transport problem. Finally, the method is demonstrated on a simulation dataset, three real-world benchmark datasets and further applied to personalized attribute predictions.
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NeurIPS - Generalized Block-Diagonal Structure Pursuit: Learning Soft Latent Task Assignment against Negative Transfer
2019Co-Authors: Zhiyong Yang, Yangbangyan Jiang, Xiaochun Cao, Qingming HuangAbstract:In multi-task learning, a major challenge springs from a notorious issue known as negative transfer, which refers to the phenomenon that sharing the knowledge with dissimilar and hard tasks often results in a worsened performance. To circumvent this issue, we propose a novel multi-task learning method, which simultaneously learns latent task representations and a block-Diagonal Latent Task Assignment Matrix (LTAM). Different from most of the previous work, pursuing the Block-Diagonal Structure of LTAM (assigning latent tasks to output tasks) alleviates negative transfer via collaboratively grouping latent tasks and output tasks such that inter-group knowledge transfer and sharing is suppressed. This goal is challenging, since 1) our notion of Block-Diagonal Property extends the traditional notion for square matrices where the $i$-th column and the $i$-th column represents the same concept; 2) marginal constraints on rows and columns are also required for avoiding isolated latent/output tasks. Facing such challenges, we propose a novel regularizer by means of an equivalent spectral condition realizing this generalized block-Diagonal property. Practically, we provide a relaxation scheme which improves the flexibility of the model. With the objective function given, we then propose an alternating optimization method, which not only tells how negative transfer is alleviated in our method but also reveals an interesting connection between our method and the optimal transport problem. Finally, the method is demonstrated on a simulation dataset, three real-world benchmark datasets and further applied to personalized attribute predictions.
Shaoming Pan - One of the best experts on this subject based on the ideXlab platform.
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robust spatial spectral block Diagonal Structure representation with fuzzy class probability for hyperspectral image classification
IEEE Transactions on Geoscience and Remote Sensing, 2020Co-Authors: Yun Ding, Shaoming Pan, Yanwen ChongAbstract:Generally, the apparent spectral information of hyperspectral images (HSIs) is directly used to measure the similarity among HSI pixels in the feature space, but this process cannot reveal the inherent characteristics of HSI pixels. Moreover, constructing spatial–spectral block-Diagonal subspace Structure representations of intraclass land-cover samples remains a challenge for low-rank representation (LRR) in HSI classification. In this article, we propose two methods to reveal the complex intrinsic spatial–spectral features of HSIs using block-Diagonal subspace Structures, namely, the spatial–spectral block-Diagonal Structure representation with class probability (SSBDCP) and spatial-spectral block-Diagonal Structure representation with fuzzy class probability (SSBDFCP) methods, for HSI classification. First, the SSBDFCP and SSBDCP methods explore the Structure similarity characteristics of the latent subspace to form a block-Diagonal LRR (BDLRR) of intraclass pixels with class probability and fuzzy class probability (FCP) and suppress the interclass pixels’ representations. Then, the spatial information is considered in the proposed methods to enhance spatial–spectral graph expression and capture more comprehensive information. Note that SSBDFCP can perform better than SSBDCP because the FCP considers the “typicalness” that a sample belongs to a specific category and utilizes complex intrinsic discriminative information based on the feedback of “weakly” supervised information. Moreover, the feedback information can remove the noise features around the pixels and take advantage of the benefits of true neighbors. The experimental results for the Indian Pines and Pavia University data sets show that the SSBDFCP and SSBDCP methods achieve better HSI classification results than other popular graph construction methods.
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Robust Spatial–Spectral Block-Diagonal Structure Representation With Fuzzy Class Probability for Hyperspectral Image Classification
IEEE Transactions on Geoscience and Remote Sensing, 2020Co-Authors: Yun Ding, Shaoming Pan, Yanwen ChongAbstract:Generally, the apparent spectral information of hyperspectral images (HSIs) is directly used to measure the similarity among HSI pixels in the feature space, but this process cannot reveal the inherent characteristics of HSI pixels. Moreover, constructing spatial–spectral block-Diagonal subspace Structure representations of intraclass land-cover samples remains a challenge for low-rank representation (LRR) in HSI classification. In this article, we propose two methods to reveal the complex intrinsic spatial–spectral features of HSIs using block-Diagonal subspace Structures, namely, the spatial–spectral block-Diagonal Structure representation with class probability (SSBDCP) and spatial-spectral block-Diagonal Structure representation with fuzzy class probability (SSBDFCP) methods, for HSI classification. First, the SSBDFCP and SSBDCP methods explore the Structure similarity characteristics of the latent subspace to form a block-Diagonal LRR (BDLRR) of intraclass pixels with class probability and fuzzy class probability (FCP) and suppress the interclass pixels’ representations. Then, the spatial information is considered in the proposed methods to enhance spatial–spectral graph expression and capture more comprehensive information. Note that SSBDFCP can perform better than SSBDCP because the FCP considers the “typicalness” that a sample belongs to a specific category and utilizes complex intrinsic discriminative information based on the feedback of “weakly” supervised information. Moreover, the feedback information can remove the noise features around the pixels and take advantage of the benefits of true neighbors. The experimental results for the Indian Pines and Pavia University data sets show that the SSBDFCP and SSBDCP methods achieve better HSI classification results than other popular graph construction methods.
Zhiyong Yang - One of the best experts on this subject based on the ideXlab platform.
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generalized block Diagonal Structure pursuit learning soft latent task assignment against negative transfer
Neural Information Processing Systems, 2019Co-Authors: Zhiyong Yang, Yangbangyan Jiang, Xiaochun Cao, Qingming HuangAbstract:In multi-task learning, a major challenge springs from a notorious issue known as negative transfer, which refers to the phenomenon that sharing the knowledge with dissimilar and hard tasks often results in a worsened performance. To circumvent this issue, we propose a novel multi-task learning method, which simultaneously learns latent task representations and a block-Diagonal Latent Task Assignment Matrix (LTAM). Different from most of the previous work, pursuing the Block-Diagonal Structure of LTAM (assigning latent tasks to output tasks) alleviates negative transfer via collaboratively grouping latent tasks and output tasks such that inter-group knowledge transfer and sharing is suppressed. This goal is challenging, since 1) our notion of Block-Diagonal Property extends the traditional notion for square matrices where the $i$-th column and the $i$-th column represents the same concept; 2) marginal constraints on rows and columns are also required for avoiding isolated latent/output tasks. Facing such challenges, we propose a novel regularizer by means of an equivalent spectral condition realizing this generalized block-Diagonal property. Practically, we provide a relaxation scheme which improves the flexibility of the model. With the objective function given, we then propose an alternating optimization method, which not only tells how negative transfer is alleviated in our method but also reveals an interesting connection between our method and the optimal transport problem. Finally, the method is demonstrated on a simulation dataset, three real-world benchmark datasets and further applied to personalized attribute predictions.
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NeurIPS - Generalized Block-Diagonal Structure Pursuit: Learning Soft Latent Task Assignment against Negative Transfer
2019Co-Authors: Zhiyong Yang, Yangbangyan Jiang, Xiaochun Cao, Qingming HuangAbstract:In multi-task learning, a major challenge springs from a notorious issue known as negative transfer, which refers to the phenomenon that sharing the knowledge with dissimilar and hard tasks often results in a worsened performance. To circumvent this issue, we propose a novel multi-task learning method, which simultaneously learns latent task representations and a block-Diagonal Latent Task Assignment Matrix (LTAM). Different from most of the previous work, pursuing the Block-Diagonal Structure of LTAM (assigning latent tasks to output tasks) alleviates negative transfer via collaboratively grouping latent tasks and output tasks such that inter-group knowledge transfer and sharing is suppressed. This goal is challenging, since 1) our notion of Block-Diagonal Property extends the traditional notion for square matrices where the $i$-th column and the $i$-th column represents the same concept; 2) marginal constraints on rows and columns are also required for avoiding isolated latent/output tasks. Facing such challenges, we propose a novel regularizer by means of an equivalent spectral condition realizing this generalized block-Diagonal property. Practically, we provide a relaxation scheme which improves the flexibility of the model. With the objective function given, we then propose an alternating optimization method, which not only tells how negative transfer is alleviated in our method but also reveals an interesting connection between our method and the optimal transport problem. Finally, the method is demonstrated on a simulation dataset, three real-world benchmark datasets and further applied to personalized attribute predictions.