J
Jian Xiong
Researcher at Southwestern University of Finance and Economics
Publications - 34
Citations - 1367
Jian Xiong is an academic researcher from Southwestern University of Finance and Economics. The author has contributed to research in topics: Evolutionary algorithm & Job shop scheduling. The author has an hindex of 13, co-authored 34 publications receiving 869 citations. Previous affiliations of Jian Xiong include National University of Defense Technology & University of New South Wales.
Papers
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A Knowledge-Based Ant Colony Optimization for Flexible Job Shop Scheduling Problems
TL;DR: A Knowledge-Based Ant Colony Optimization (KBACO) algorithm is proposed in this paper for the Flexible Job Shop Scheduling Problem (FJSSP) and results indicate that the proposed KBACO algorithm outperforms some current approaches in the quality of schedules.
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Robust scheduling for multi-objective flexible job-shop problems with random machine breakdowns
TL;DR: A multi-objective evolutionary algorithm to address robust scheduling for a flexible job-shop scheduling problem with random machine breakdowns and results indicate that the first suggested surrogate measure performs better for small cases, while the second surrogate measure performing better for both small and relatively large cases.
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Feature Selective Projection with Low-Rank Embedding and Dual Laplacian Regularization
Chang Tang,Xinwang Liu,Xinzhong Zhu,Jian Xiong,Miaomiao Li,Jingyuan Xia,Xiangke Wang,Lizhe Wang +7 more
TL;DR: This paper designs an unsupervised linear feature selective projection (FSP) for feature extraction with low-rank embedding and dual Laplacian regularization, with the aim to exploit the intrinsic relationship among data and suppress the impact of noise.
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Efficient and Effective Regularized Incomplete Multi-View Clustering
TL;DR: This paper proposes an Efficient and Effective Incomplete Multi-view Clustering (EE-IMVC) algorithm, which proposes to impute each incomplete base matrix generated by incomplete views with a learned consensus clustering matrix to address issues of intensive computational and storage complexities, over-complicated optimization and limitedly improved clustering performance.
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Cross-view Locality Preserved Diversity and Consensus Learning for Multi-view Unsupervised Feature Selection
TL;DR: This work resent a MV-UFS model via cross-view local structure preserved diversity and consensus learning, referred to as CvLP-DCL briefly, and regularize the fact that different views represent same samples to solve the resultant optimization problem.