The Experts below are selected from a list of 318 Experts worldwide ranked by ideXlab platform

Yuxin Peng - One of the best experts on this subject based on the ideXlab platform.

  • unified Constraint Propagation on multi view data
    National Conference on Artificial Intelligence, 2013
    Co-Authors: Yuxin Peng
    Abstract:

    This paper presents a unified framework for intra-view and inter-view Constraint Propagation on multiview data. Pairwise Constraint Propagation has been studied extensively, where each pairwise Constraint is defined over a pair of data points from a single view. In contrast, very little attention has been paid to interview Constraint Propagation, which is more challenging since each pairwise Constraint is now defined over a pair of data points from different views. Although both intraview and inter-view Constraint Propagation are crucial for multi-view tasks, most previous methods can not handle them simultaneously. To address this challenging issue, we propose to decompose these two types of Constraint Propagation into semi-supervised learning subproblems so that they can be uniformly solved based on the traditional label Propagation techniques. To further integrate them into a unified framework, we utilize the results of intra-view Constraint Propagation to adjust the similarity matrix of each view and then perform inter-view Constraint Propagation with the adjusted similarity matrices. The experimental results in cross-view retrieval have shown the superior performance of our unified Constraint Propagation.

  • AAAI - Unified Constraint Propagation on multi-view data
    2013
    Co-Authors: Yuxin Peng
    Abstract:

    This paper presents a unified framework for intra-view and inter-view Constraint Propagation on multiview data. Pairwise Constraint Propagation has been studied extensively, where each pairwise Constraint is defined over a pair of data points from a single view. In contrast, very little attention has been paid to interview Constraint Propagation, which is more challenging since each pairwise Constraint is now defined over a pair of data points from different views. Although both intraview and inter-view Constraint Propagation are crucial for multi-view tasks, most previous methods can not handle them simultaneously. To address this challenging issue, we propose to decompose these two types of Constraint Propagation into semi-supervised learning subproblems so that they can be uniformly solved based on the traditional label Propagation techniques. To further integrate them into a unified framework, we utilize the results of intra-view Constraint Propagation to adjust the similarity matrix of each view and then perform inter-view Constraint Propagation with the adjusted similarity matrices. The experimental results in cross-view retrieval have shown the superior performance of our unified Constraint Propagation.

  • Exhaustive and Efficient Constraint Propagation: A Graph-Based Learning Approach and Its Applications
    International Journal of Computer Vision, 2012
    Co-Authors: Yuxin Peng
    Abstract:

    This paper presents a novel pairwise Constraint Propagation approach by decomposing the challenging Constraint Propagation problem into a set of independent semi-supervised classification subproblems which can be solved in quadratic time using label Propagation based on $$k$$ -nearest neighbor graphs. Considering that this time cost is proportional to the number of all possible pairwise Constraints, our approach actually provides an efficient solution for exhaustively propagating pairwise Constraints throughout the entire dataset. The resulting exhaustive set of propagated pairwise Constraints are further used to adjust the similarity matrix for constrained spectral clustering. Other than the traditional Constraint Propagation on single-source data, our approach is also extended to more challenging Constraint Propagation on multi-source data where each pairwise Constraint is defined over a pair of data points from different sources. This multi-source Constraint Propagation has an important application to cross-modal multimedia retrieval. Extensive results have shown the superior performance of our approach.

  • ICDM - Heterogeneous Constraint Propagation with Constrained Sparse Representation
    2012 IEEE 12th International Conference on Data Mining, 2012
    Co-Authors: Yuxin Peng
    Abstract:

    This paper presents a graph-based method for heterogeneous Constraint Propagation on multi-modal data using constrained sparse representation. Since heterogeneous pair wise Constraints are defined over pairs of data points from different modalities, heterogeneous Constraint Propagation is more challenging than the transitional homogeneous Constraint Propagation on single-modal data which has been studied extensively in previous work. The main difficulty of heterogeneous Constraint Propagation lies in how to effectively propagate heterogeneous pair wise Constraints across different modalities. To address this issue, we decompose heterogeneous Constraint Propagation into semi-supervised learning sub problems which can then be efficiently solved by graph-based label Propagation. Moreover, we develop a constrained sparse representation method for graph construction over each modality using homogeneous pair wise Constraints. The experimental results in cross-modal retrieval have shown the superior performance of our heterogeneous Constraint Propagation.

  • exhaustive and efficient Constraint Propagation a semi supervised learning perspective and its applications
    arXiv: Artificial Intelligence, 2011
    Co-Authors: Yuxin Peng
    Abstract:

    This paper presents a novel pairwise Constraint Propagation approach by decomposing the challenging Constraint Propagation problem into a set of independent semi-supervised learning subproblems which can be solved in quadratic time using label Propagation based on k-nearest neighbor graphs. Considering that this time cost is proportional to the number of all possible pairwise Constraints, our approach actually provides an efficient solution for exhaustively propagating pairwise Constraints throughout the entire dataset. The resulting exhaustive set of propagated pairwise Constraints are further used to adjust the similarity matrix for constrained spectral clustering. Other than the traditional Constraint Propagation on single-source data, our approach is also extended to more challenging Constraint Propagation on multi-source data where each pairwise Constraint is defined over a pair of data points from different sources. This multi-source Constraint Propagation has an important application to cross-modal multimedia retrieval. Extensive results have shown the superior performance of our approach.

Pierre Lopez - One of the best experts on this subject based on the ideXlab platform.

  • Generalized disjunctive Constraint Propagation for solving the job shop problem with time lags
    Engineering Applications of Artificial Intelligence, 2011
    Co-Authors: Christian Artigues, Marie-josé Huguet, Pierre Lopez
    Abstract:

    This paper addresses the job-shop scheduling problem with time-lags. We propose an insertion heuristic and generalized resource Constraint Propagation mechanisms. Our propositions are embedded in a branch-and-bound algorithm to provide an experimental evaluation on some benchmark instances. The results obtained conclude that our heuristic achieves the best solutions on the instances, especially when problems involve tightened time lags. The results also prove the interest of the Constraint Propagation generalization when time lags are considered.

  • Generalized disjunctive Constraint Propagation for solving the job shop problem with time lags
    Engineering Applications of Artificial Intelligence, 2010
    Co-Authors: Christian Artigues, Marie-josé Huguet, Pierre Lopez
    Abstract:

    In this paper we propose new insights based on an insertion heuristic and generalized resource Constraint Propagation for solving the job shop scheduling problem with minimum and maximum time-lags. To show the contribution of our propositions we propose a branch-and-bound algorithm and provide an experimental study. The results obtained conclude that our heuristic obtains feasible schedules with a better makespan than previous approaches, especially for instances with tightened time lags. The results also prove the interest of the Constraint Propagation generalization when time lags are considered.

Christian Artigues - One of the best experts on this subject based on the ideXlab platform.

  • Generalized disjunctive Constraint Propagation for solving the job shop problem with time lags
    Engineering Applications of Artificial Intelligence, 2011
    Co-Authors: Christian Artigues, Marie-josé Huguet, Pierre Lopez
    Abstract:

    This paper addresses the job-shop scheduling problem with time-lags. We propose an insertion heuristic and generalized resource Constraint Propagation mechanisms. Our propositions are embedded in a branch-and-bound algorithm to provide an experimental evaluation on some benchmark instances. The results obtained conclude that our heuristic achieves the best solutions on the instances, especially when problems involve tightened time lags. The results also prove the interest of the Constraint Propagation generalization when time lags are considered.

  • Generalized disjunctive Constraint Propagation for solving the job shop problem with time lags
    Engineering Applications of Artificial Intelligence, 2010
    Co-Authors: Christian Artigues, Marie-josé Huguet, Pierre Lopez
    Abstract:

    In this paper we propose new insights based on an insertion heuristic and generalized resource Constraint Propagation for solving the job shop scheduling problem with minimum and maximum time-lags. To show the contribution of our propositions we propose a branch-and-bound algorithm and provide an experimental study. The results obtained conclude that our heuristic obtains feasible schedules with a better makespan than previous approaches, especially for instances with tightened time lags. The results also prove the interest of the Constraint Propagation generalization when time lags are considered.

Patrick H. Ngai - One of the best experts on this subject based on the ideXlab platform.

  • Embedding temporal Constraint Propagation in machine sequencing for job shop scheduling
    Artificial Intelligence for Engineering Design Analysis and Manufacturing, 1993
    Co-Authors: Wesley W. Chu, Patrick H. Ngai
    Abstract:

    In this paper, we show how a temporal Constraint Propagation technique can be embedded in the machine sequencing approach for solving the job shop scheduling problem. The temporal Constraint Propagation algorithm propagates the precedence Constraints and machine interference Constraints to reduce the search space generated by the machine sequencing approach. Further, by making use of the temporal nature of the job shop scheduling, efficient algorithms to propagate precedence Constraints and machine interference Constraints are developed. Experimental results reveal that embedding Constraint Propagation in the machine sequencing approach significantly reduces the computation time more than by just using the machine sequencing approach alone. Further, the proposed temporal Constraint Propagation algorithms provide an order of magnitude improvement on the computation time over the conventional Constraint Propagation algorithm.

  • KBCS - Solving the Generalized Job Shop Scheduling Problem via Temporal Constraint Propagation
    Knowledge Based Computer Systems, 1
    Co-Authors: Wesley W. Chu, Patrick H. Ngai
    Abstract:

    A Scheduling Algorithm with Temporal Constraint Propagation (STCP) is proposed to solve the generalized job shop scheduling problem. STCP propagates the precedence Constraints and machine interference Constraints to reduce the search space of the active schedules. Further, by making use of the temporal nature of the job shop scheduling, efficient algorithms to propagate precedence Constraints and machine interference Constraints are developed. Experimental results reveal that Constraint Propagation significantly reduces the computation time for scheduling. Further, the proposed temporal Constraint Propagation algorithms provide an order of magnitude improvement on the computation time over the conventional Constraint Propagation algorithm.

Marie-josé Huguet - One of the best experts on this subject based on the ideXlab platform.

  • Generalized disjunctive Constraint Propagation for solving the job shop problem with time lags
    Engineering Applications of Artificial Intelligence, 2011
    Co-Authors: Christian Artigues, Marie-josé Huguet, Pierre Lopez
    Abstract:

    This paper addresses the job-shop scheduling problem with time-lags. We propose an insertion heuristic and generalized resource Constraint Propagation mechanisms. Our propositions are embedded in a branch-and-bound algorithm to provide an experimental evaluation on some benchmark instances. The results obtained conclude that our heuristic achieves the best solutions on the instances, especially when problems involve tightened time lags. The results also prove the interest of the Constraint Propagation generalization when time lags are considered.

  • Generalized disjunctive Constraint Propagation for solving the job shop problem with time lags
    Engineering Applications of Artificial Intelligence, 2010
    Co-Authors: Christian Artigues, Marie-josé Huguet, Pierre Lopez
    Abstract:

    In this paper we propose new insights based on an insertion heuristic and generalized resource Constraint Propagation for solving the job shop scheduling problem with minimum and maximum time-lags. To show the contribution of our propositions we propose a branch-and-bound algorithm and provide an experimental study. The results obtained conclude that our heuristic obtains feasible schedules with a better makespan than previous approaches, especially for instances with tightened time lags. The results also prove the interest of the Constraint Propagation generalization when time lags are considered.