The Experts below are selected from a list of 21603 Experts worldwide ranked by ideXlab platform

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

  • a Convergent Solution to tensor subspace learning
    International Joint Conference on Artificial Intelligence, 2007
    Co-Authors: Huan Wang, Shuicheng Yan, Thomas S Huang, Xiaoou Tang
    Abstract:

    Recently, substantial efforts have been devoted to the subspace learning techniques based on tensor representation, such as 2DLDA [Ye et al., 2004], DATER [Yan et al., 2005] and Tensor Subspace Analysis (TSA) [He et al., 2005]. In this context, a vital yet unsolved problem is that the computational convergency of these iterative algorithms is not guaranteed. In this work, we present a novel Solution procedure for general tensor-based subspace learning, followed by a detailed convergency proof of the Solution projection matrices and the objective function value. Extensive experiments on real-world databases verify the high convergence speed of the proposed procedure, as well as its superiority in classification capability over traditional Solution procedures.

  • IJCAI - A Convergent Solution to tensor subspace learning
    2007
    Co-Authors: Huan Wang, Shuicheng Yan, Thomas S Huang, Xiaoou Tang
    Abstract:

    Recently, substantial efforts have been devoted to the subspace learning techniques based on tensor representation, such as 2DLDA [Ye et al., 2004], DATER [Yan et al., 2005] and Tensor Subspace Analysis (TSA) [He et al., 2005]. In this context, a vital yet unsolved problem is that the computational convergency of these iterative algorithms is not guaranteed. In this work, we present a novel Solution procedure for general tensor-based subspace learning, followed by a detailed convergency proof of the Solution projection matrices and the objective function value. Extensive experiments on real-world databases verify the high convergence speed of the proposed procedure, as well as its superiority in classification capability over traditional Solution procedures.

Alessandro Astolfi - One of the best experts on this subject based on the ideXlab platform.

  • a Convergent Solution to the multi vehicle coverage problem
    American Control Conference, 2013
    Co-Authors: Adnan Tahirovic, Alessandro Astolfi
    Abstract:

    The paper presents a new Solution to the multi-vehicle coverage problem. The proposed algorithm guarantees complete coverage and provides collaborative behaviors of vehicles, despite the fact that it does not explicitly exploit any computationally intensive optimization technique. The algorithm can deal with any mission domain, including regions with irregular shapes, multi-connected and disjoint regions. It gives reasonably good Solutions even for partially connected multi-vehicle systems. The coverage problem for regions the shape of which change in time regardless the vehicle movement is also solved by the proposed algorithm.

  • ACC - A Convergent Solution to the multi-vehicle coverage problem
    2013 American Control Conference, 2013
    Co-Authors: Adnan Tahirovic, Alessandro Astolfi
    Abstract:

    The paper presents a new Solution to the multi-vehicle coverage problem. The proposed algorithm guarantees complete coverage and provides collaborative behaviors of vehicles, despite the fact that it does not explicitly exploit any computationally intensive optimization technique. The algorithm can deal with any mission domain, including regions with irregular shapes, multi-connected and disjoint regions. It gives reasonably good Solutions even for partially connected multi-vehicle systems. The coverage problem for regions the shape of which change in time regardless the vehicle movement is also solved by the proposed algorithm.

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

  • a Convergent Solution to tensor subspace learning
    International Joint Conference on Artificial Intelligence, 2007
    Co-Authors: Huan Wang, Shuicheng Yan, Thomas S Huang, Xiaoou Tang
    Abstract:

    Recently, substantial efforts have been devoted to the subspace learning techniques based on tensor representation, such as 2DLDA [Ye et al., 2004], DATER [Yan et al., 2005] and Tensor Subspace Analysis (TSA) [He et al., 2005]. In this context, a vital yet unsolved problem is that the computational convergency of these iterative algorithms is not guaranteed. In this work, we present a novel Solution procedure for general tensor-based subspace learning, followed by a detailed convergency proof of the Solution projection matrices and the objective function value. Extensive experiments on real-world databases verify the high convergence speed of the proposed procedure, as well as its superiority in classification capability over traditional Solution procedures.

  • IJCAI - A Convergent Solution to tensor subspace learning
    2007
    Co-Authors: Huan Wang, Shuicheng Yan, Thomas S Huang, Xiaoou Tang
    Abstract:

    Recently, substantial efforts have been devoted to the subspace learning techniques based on tensor representation, such as 2DLDA [Ye et al., 2004], DATER [Yan et al., 2005] and Tensor Subspace Analysis (TSA) [He et al., 2005]. In this context, a vital yet unsolved problem is that the computational convergency of these iterative algorithms is not guaranteed. In this work, we present a novel Solution procedure for general tensor-based subspace learning, followed by a detailed convergency proof of the Solution projection matrices and the objective function value. Extensive experiments on real-world databases verify the high convergence speed of the proposed procedure, as well as its superiority in classification capability over traditional Solution procedures.

Adnan Tahirovic - One of the best experts on this subject based on the ideXlab platform.

  • a Convergent Solution to the multi vehicle coverage problem
    American Control Conference, 2013
    Co-Authors: Adnan Tahirovic, Alessandro Astolfi
    Abstract:

    The paper presents a new Solution to the multi-vehicle coverage problem. The proposed algorithm guarantees complete coverage and provides collaborative behaviors of vehicles, despite the fact that it does not explicitly exploit any computationally intensive optimization technique. The algorithm can deal with any mission domain, including regions with irregular shapes, multi-connected and disjoint regions. It gives reasonably good Solutions even for partially connected multi-vehicle systems. The coverage problem for regions the shape of which change in time regardless the vehicle movement is also solved by the proposed algorithm.

  • ACC - A Convergent Solution to the multi-vehicle coverage problem
    2013 American Control Conference, 2013
    Co-Authors: Adnan Tahirovic, Alessandro Astolfi
    Abstract:

    The paper presents a new Solution to the multi-vehicle coverage problem. The proposed algorithm guarantees complete coverage and provides collaborative behaviors of vehicles, despite the fact that it does not explicitly exploit any computationally intensive optimization technique. The algorithm can deal with any mission domain, including regions with irregular shapes, multi-connected and disjoint regions. It gives reasonably good Solutions even for partially connected multi-vehicle systems. The coverage problem for regions the shape of which change in time regardless the vehicle movement is also solved by the proposed algorithm.

M. F. Alam - One of the best experts on this subject based on the ideXlab platform.