The Experts below are selected from a list of 2748 Experts worldwide ranked by ideXlab platform

Qu Annie - One of the best experts on this subject based on the ideXlab platform.

  • Dynamic Tensor Recommender Systems
    2020
    Co-Authors: Zhang Yanqing, Bi Xuan, Tang Niansheng, Qu Annie
    Abstract:

    Recommender systems have been extensively used by the entertainment industry, business marketing and the biomedical industry. In addition to its capacity of providing preference-based recommendations as an unsupervised learning methodology, it has been also proven useful in sales forecasting, product introduction and other production related businesses. Since some consumers and companies need a recommendation or prediction for future budget, labor and supply chain coordination, dynamic recommender systems for precise forecasting have become extremely necessary. In this article, we propose a new recommendation method, namely the dynamic tensor recommender system (DTRS), which aims particularly at forecasting future recommendation. The proposed method utilizes a tensor-valued function of time to integrate time and contextual information, and creates a time-varying Coefficient model for temporal tensor factorization through a polynomial Spline approximation. Major advantages of the proposed method include competitive future recommendation predictions and effective prediction interval estimations. In theory, we establish the convergence rate of the proposed tensor factorization and asymptotic normality of the Spline Coefficient estimator. The proposed method is applied to simulations and IRI marketing data. Numerical studies demonstrate that the proposed method outperforms existing methods in terms of future time forecasting

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

  • Dynamic Tensor Recommender Systems
    2020
    Co-Authors: Zhang Yanqing, Bi Xuan, Tang Niansheng, Qu Annie
    Abstract:

    Recommender systems have been extensively used by the entertainment industry, business marketing and the biomedical industry. In addition to its capacity of providing preference-based recommendations as an unsupervised learning methodology, it has been also proven useful in sales forecasting, product introduction and other production related businesses. Since some consumers and companies need a recommendation or prediction for future budget, labor and supply chain coordination, dynamic recommender systems for precise forecasting have become extremely necessary. In this article, we propose a new recommendation method, namely the dynamic tensor recommender system (DTRS), which aims particularly at forecasting future recommendation. The proposed method utilizes a tensor-valued function of time to integrate time and contextual information, and creates a time-varying Coefficient model for temporal tensor factorization through a polynomial Spline approximation. Major advantages of the proposed method include competitive future recommendation predictions and effective prediction interval estimations. In theory, we establish the convergence rate of the proposed tensor factorization and asymptotic normality of the Spline Coefficient estimator. The proposed method is applied to simulations and IRI marketing data. Numerical studies demonstrate that the proposed method outperforms existing methods in terms of future time forecasting

G Sharp - One of the best experts on this subject based on the ideXlab platform.

  • analytic regularization of uniform cubic b Spline deformation fields
    Medical Image Computing and Computer-Assisted Intervention, 2012
    Co-Authors: James A Shackleford, Qi Yang, Ana Lourenco, Nadya Shusharina, Nagarajan Kandasamy, G Sharp
    Abstract:

    Image registration is inherently ill-posed, and lacks a unique solution. In the context of medical applications, it is desirable to avoid solutions that describe physically unsound deformations within the patient anatomy. Among the accepted methods of regularizing non-rigid image registration to provide solutions applicable to medical practice is the penalty of thin-plate bending energy. In this paper, we develop an exact, analytic method for computing the bending energy of a three-dimensional B-Spline deformation field as a quadratic matrix operation on the Spline Coefficient values. Results presented on ten thoracic case studies indicate the analytic solution is between 61---1371x faster than a numerical central differencing solution.

Bi Xuan - One of the best experts on this subject based on the ideXlab platform.

  • Dynamic Tensor Recommender Systems
    2020
    Co-Authors: Zhang Yanqing, Bi Xuan, Tang Niansheng, Qu Annie
    Abstract:

    Recommender systems have been extensively used by the entertainment industry, business marketing and the biomedical industry. In addition to its capacity of providing preference-based recommendations as an unsupervised learning methodology, it has been also proven useful in sales forecasting, product introduction and other production related businesses. Since some consumers and companies need a recommendation or prediction for future budget, labor and supply chain coordination, dynamic recommender systems for precise forecasting have become extremely necessary. In this article, we propose a new recommendation method, namely the dynamic tensor recommender system (DTRS), which aims particularly at forecasting future recommendation. The proposed method utilizes a tensor-valued function of time to integrate time and contextual information, and creates a time-varying Coefficient model for temporal tensor factorization through a polynomial Spline approximation. Major advantages of the proposed method include competitive future recommendation predictions and effective prediction interval estimations. In theory, we establish the convergence rate of the proposed tensor factorization and asymptotic normality of the Spline Coefficient estimator. The proposed method is applied to simulations and IRI marketing data. Numerical studies demonstrate that the proposed method outperforms existing methods in terms of future time forecasting

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

  • Dynamic Tensor Recommender Systems
    2020
    Co-Authors: Zhang Yanqing, Bi Xuan, Tang Niansheng, Qu Annie
    Abstract:

    Recommender systems have been extensively used by the entertainment industry, business marketing and the biomedical industry. In addition to its capacity of providing preference-based recommendations as an unsupervised learning methodology, it has been also proven useful in sales forecasting, product introduction and other production related businesses. Since some consumers and companies need a recommendation or prediction for future budget, labor and supply chain coordination, dynamic recommender systems for precise forecasting have become extremely necessary. In this article, we propose a new recommendation method, namely the dynamic tensor recommender system (DTRS), which aims particularly at forecasting future recommendation. The proposed method utilizes a tensor-valued function of time to integrate time and contextual information, and creates a time-varying Coefficient model for temporal tensor factorization through a polynomial Spline approximation. Major advantages of the proposed method include competitive future recommendation predictions and effective prediction interval estimations. In theory, we establish the convergence rate of the proposed tensor factorization and asymptotic normality of the Spline Coefficient estimator. The proposed method is applied to simulations and IRI marketing data. Numerical studies demonstrate that the proposed method outperforms existing methods in terms of future time forecasting