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Xiujun Z.,Sichuan University | Xiujun Z.,Key Laboratory of Pattern Recognition and Intelligent Information Processing in Sichuan | Chang L.,Sichuan University | Chang L.,Key Laboratory of Pattern Recognition and Intelligent Information Processing in Sichuan
The Scientific World Journal | Year: 2014

In order to overcome the limitation of traditional nonnegative factorization algorithms, the paper presents a generalized discriminant orthogonal non-negative tensor factorization algorithm. At first, the algorithm takes the orthogonal constraint into account to ensure the nonnegativity of the low-dimensional features. Furthermore, the discriminant constraint is imposed on low-dimensional weights to strengthen the discriminant capability of the low-dimensional features. The experiments on facial expression recognition have demonstrated that the algorithm is superior to other non-negative factorization algorithms. © 2014 Zhang XiuJun and Liu Chang. Source


Chang L.,University Lumiere Lyon 2 | Chang L.,Key Laboratory of Pattern Recognition and Intelligent Information Processing in Sichuan | Chang L.,Sichuan University | Ouzrout Y.,University Lumiere Lyon 2 | And 4 more authors.
International Journal of Production Economics | Year: 2014

Decision making is a core problem in Supply Chains. A large number of studies in literature have reported various decision making techniques based on customers' requirements. Taking into account high risk transactions in virtual Supply Chain market, trust is a very critical element and should be treated as an important reference when customers try to select proper suppliers. Recently, a great effort has been carried out to develop decision making based on trust and reputation. However, these research works still stay on the stage of theoretical research. This paper presents and implements a multi-criteria decision making approach based on trust and reputation in Supply Chain. Firstly, this paper defines general trust indicators in real Supply Chain settings, and designs a multi-dimensional trust and reputation model. This paper also introduces K-mean clustering algorithm to remove unfair rating scores. Then, based on this trust and reputation model, we propose a multi-criteria decision making approach based on variable weights and satisfaction principle. In order to validate the performance of this approach, we simulate a practical Supply Chain setting with multi-agents platform. The simulation experiments demonstrate that the proposed trust and reputation model can effectively filter unfair ratings from those customers who did lie and the proposed multi-criteria decision making method can help customers make right decisions. © 2013 Elsevier B.V. Source


Chang L.,Chengdu University of Technology | Chang L.,Sichuan University | Chang L.,Key Laboratory of Pattern Recognition and Intelligent Information Processing in Sichuan | Ouzrout Y.,Chengdu University of Technology | And 8 more authors.
Mathematical Problems in Engineering | Year: 2013

Nowadays, a large number of reputation systems have been deployed in practical applications or investigated in the literature to protect buyers from deception and malicious behaviors in online transactions. As an efficient Bayesian analysis tool, Hidden Markov Model (HMM) has been used into e-commerce to describe the dynamic behavior of sellers. Traditional solutions adopt Baum-Welch algorithm to train model parameters which is unstable due to its inability to find a globally optimal solution. Consequently, this paper presents a reputation evaluation mechanism based on the optimized Hidden Markov Model, which is called PSOHMM. The algorithm takes full advantage of the search mechanism in Particle Swarm Optimization (PSO) algorithm to strengthen the learning ability of HMM and PSO has been modified to guarantee interval and normalization constraints in HMM. Furthermore, a simplified reputation evaluation framework based on HMM is developed and applied to analyze the specific behaviors of sellers. The simulation experiments demonstrate that the proposed PSOHMM has better performance to search optimal model parameters than BWHMM, has faster convergence speed, and is more stable than BWHMM. Compared with Average and Beta reputation evaluation mechanism, PSOHMM can reflect the behavior changes of sellers more quickly in e-commerce systems. © 2013 Liu Chang et al. Source


Chang L.,Sichuan University | Chang L.,Key Laboratory of Pattern Recognition and Intelligent Information Processing in Sichuan | Weidong Z.,Sichuan University | Weidong Z.,Key Laboratory of Pattern Recognition and Intelligent Information Processing in Sichuan | And 6 more authors.
Computational Intelligence and Neuroscience | Year: 2015

To study incremental machine learning in tensor space, this paper proposes incremental tensor discriminant analysis. The algorithm employs tensor representation to carry on discriminant analysis and combine incremental learning to alleviate the computational cost. This paper proves that the algorithm can be unified into the graph framework theoretically and analyzes the time and space complexity in detail. The experiments on facial image detection have shown that the algorithm not only achieves sound performance compared with other algorithms, but also reduces the computational issues apparently. © 2015 Liu Chang et al. Source

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