The Experts below are selected from a list of 18081 Experts worldwide ranked by ideXlab platform
Weixing Song - One of the best experts on this subject based on the ideXlab platform.
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Robust mixture multivariate linear regression by multivariate Laplace Distribution
Statistics & Probability Letters, 2017Co-Authors: Xiuqin Bai, Weixing SongAbstract:Abstract Assuming that the error terms follow a multivariate Laplace Distribution, we propose a robust estimation procedure for mixture of multivariate linear regression models in this paper. Using the fact that the multivariate Laplace Distribution is a scale mixture of the multivariate standard normal Distribution, an efficient EM algorithm is designed to implement the proposed robust estimation procedure. The performance of the proposed algorithm is thoroughly evaluated by some simulation and comparison studies.
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robust mixture regression model fitting by Laplace Distribution
Computational Statistics & Data Analysis, 2014Co-Authors: Weixing Song, Weixin Yao, Yanru XingAbstract:A robust estimation procedure for mixture linear regression models is proposed by assuming that the error terms follow a Laplace Distribution. Using the fact that the Laplace Distribution can be written as a scale mixture of a normal and a latent Distribution, this procedure is implemented by an EM algorithm which incorporates two types of missing information from the mixture class membership and the latent variable. Finite sample performance of the proposed algorithm is evaluated by simulations. The proposed method is compared with other procedures, and a sensitivity study is also conducted based on a real data set.
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Robust errors-in-variables linear regression via Laplace Distribution
Statistics & Probability Letters, 2014Co-Authors: Jianhong Shi, Kun Chen, Weixing SongAbstract:Abstract Robust estimation procedures for linear and mixture linear errors-in-variables regression models are proposed based on the relationship between the least absolute deviation criterion and maximum likelihood estimation in a Laplace Distribution. The finite sample performance of the proposed procedures is evaluated by simulation studies.
Shinichi Satoh - One of the best experts on this subject based on the ideXlab platform.
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statistical inference of gaussian Laplace Distribution for person verification
ACM Multimedia, 2017Co-Authors: Zheng Wang, Junjun Jiang, Shinichi SatohAbstract:Metric learning is an important issue in the person verification problem, which is to identify whether a pair of face or human body images is about the same person. Due to low running cost, the non-iterative statistical inference methods for metric learning show their efficiency and effectiveness to large scale datasets and on-line updating person verification applications. The KISSME method is a typical one that constructs the metric based on two assumptions that both of the discrepancy spaces of negative pairs and positive pairs should be Gaussian structures. However, we find that, in fact, the Distribution of discrepancies of positive pairs might tend to the Laplace Distribution rather than the Gaussian Distribution. Based on this finding, we propose a metric learning method by exploiting Gaussian-Laplace Distribution statistical inference, where the Gaussian Distribution of negative discrepancies and the Laplace Distribution of positive discrepancies are considered together. Experiments conducted on two human body datasets (VIPeR and Market-1501) and one face dataset (LFW) show its superiority in terms of effectiveness and efficiency as compared with the state-of-the-art approaches, no matter the appearance description is handcrafted or deep learned.
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ACM Multimedia - Statistical Inference of Gaussian-Laplace Distribution for Person Verification
Proceedings of the 25th ACM international conference on Multimedia, 2017Co-Authors: Zheng Wang, Junjun Jiang, Shinichi SatohAbstract:Metric learning is an important issue in the person verification problem, which is to identify whether a pair of face or human body images is about the same person. Due to low running cost, the non-iterative statistical inference methods for metric learning show their efficiency and effectiveness to large scale datasets and on-line updating person verification applications. The KISSME method is a typical one that constructs the metric based on two assumptions that both of the discrepancy spaces of negative pairs and positive pairs should be Gaussian structures. However, we find that, in fact, the Distribution of discrepancies of positive pairs might tend to the Laplace Distribution rather than the Gaussian Distribution. Based on this finding, we propose a metric learning method by exploiting Gaussian-Laplace Distribution statistical inference, where the Gaussian Distribution of negative discrepancies and the Laplace Distribution of positive discrepancies are considered together. Experiments conducted on two human body datasets (VIPeR and Market-1501) and one face dataset (LFW) show its superiority in terms of effectiveness and efficiency as compared with the state-of-the-art approaches, no matter the appearance description is handcrafted or deep learned.
Zheng Wang - One of the best experts on this subject based on the ideXlab platform.
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statistical inference of gaussian Laplace Distribution for person verification
ACM Multimedia, 2017Co-Authors: Zheng Wang, Junjun Jiang, Shinichi SatohAbstract:Metric learning is an important issue in the person verification problem, which is to identify whether a pair of face or human body images is about the same person. Due to low running cost, the non-iterative statistical inference methods for metric learning show their efficiency and effectiveness to large scale datasets and on-line updating person verification applications. The KISSME method is a typical one that constructs the metric based on two assumptions that both of the discrepancy spaces of negative pairs and positive pairs should be Gaussian structures. However, we find that, in fact, the Distribution of discrepancies of positive pairs might tend to the Laplace Distribution rather than the Gaussian Distribution. Based on this finding, we propose a metric learning method by exploiting Gaussian-Laplace Distribution statistical inference, where the Gaussian Distribution of negative discrepancies and the Laplace Distribution of positive discrepancies are considered together. Experiments conducted on two human body datasets (VIPeR and Market-1501) and one face dataset (LFW) show its superiority in terms of effectiveness and efficiency as compared with the state-of-the-art approaches, no matter the appearance description is handcrafted or deep learned.
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ACM Multimedia - Statistical Inference of Gaussian-Laplace Distribution for Person Verification
Proceedings of the 25th ACM international conference on Multimedia, 2017Co-Authors: Zheng Wang, Junjun Jiang, Shinichi SatohAbstract:Metric learning is an important issue in the person verification problem, which is to identify whether a pair of face or human body images is about the same person. Due to low running cost, the non-iterative statistical inference methods for metric learning show their efficiency and effectiveness to large scale datasets and on-line updating person verification applications. The KISSME method is a typical one that constructs the metric based on two assumptions that both of the discrepancy spaces of negative pairs and positive pairs should be Gaussian structures. However, we find that, in fact, the Distribution of discrepancies of positive pairs might tend to the Laplace Distribution rather than the Gaussian Distribution. Based on this finding, we propose a metric learning method by exploiting Gaussian-Laplace Distribution statistical inference, where the Gaussian Distribution of negative discrepancies and the Laplace Distribution of positive discrepancies are considered together. Experiments conducted on two human body datasets (VIPeR and Market-1501) and one face dataset (LFW) show its superiority in terms of effectiveness and efficiency as compared with the state-of-the-art approaches, no matter the appearance description is handcrafted or deep learned.
Mark F. J. Steel - One of the best experts on this subject based on the ideXlab platform.
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Inference for grouped data with a truncated skew-Laplace Distribution
Computational Statistics & Data Analysis, 2011Co-Authors: Francisco J. Rubio, Mark F. J. SteelAbstract:The skew-Laplace Distribution has been used for modelling particle size with point observations. In reality, the observations are truncated and grouped (rounded). This must be formally taken into account for accurate modelling, and it is shown how this leads to convenient closed-form expressions for the likelihood in this model. In a Bayesian framework, we specify “noninformative” benchmark priors which only require the choice of a single scalar prior hyperparameter. We derive conditions for the existence of the posterior Distribution when rounding and various forms of truncation are considered in the model. We will focus mostly on modelling microbiological data obtained with flow cytometry using a skew-Laplace Distribution. However, we also use the model on data often used to illustrate other skewed Distributions, and we show that our modelling favourably compares with the popular and flexible skew-Student models. Further examples on simulated data illustrate the wide applicability of the model.
Xiuqin Bai - One of the best experts on this subject based on the ideXlab platform.
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Robust mixture multivariate linear regression by multivariate Laplace Distribution
Statistics & Probability Letters, 2017Co-Authors: Xiuqin Bai, Weixing SongAbstract:Abstract Assuming that the error terms follow a multivariate Laplace Distribution, we propose a robust estimation procedure for mixture of multivariate linear regression models in this paper. Using the fact that the multivariate Laplace Distribution is a scale mixture of the multivariate standard normal Distribution, an efficient EM algorithm is designed to implement the proposed robust estimation procedure. The performance of the proposed algorithm is thoroughly evaluated by some simulation and comparison studies.
