Publications

Peer-Reviewed Journal Papers

• Luhuan Fei, Weijia Lin, Jiankun Wang, Lu Sun, Mineichi Kudo and Keigo Kimura, Multi-View Multi-Label Personalized Classification via Generalized Exclusive Sparse Tensor Factorization. Knowledge and Information Systems, accepted, 2025
  
• Luhuan Fei, Xinyi Wang, Jiankun Wang, Lu Sun and Yuyao Zhang, Multi-Level Sparse Network Lasso: Locally Sparse Learning with Flexible Sample Clusters. NeuroComputing, 2025. DOI: 10.1016/j.neucom.2025.129898
  
• Zhanwang Mao, Lu Sun and Youlong Wu, Robust Multi-View Subspace Clustering with Missing Data by Aligning Nonlinear Manifolds, Pattern Recognition, 2024. DOI: 10.1016/j.patcog.2024.111280
  
• Jiankun Wang, Luhuan Fei and Lu Sun, Multi-level network Lasso for multi-task personalized learning, Pattern Recognition, 2024. DOI: 10.1016/j.patcog.2024.111213
  
• Jiaqi Bao, Mineichi Kudo, Keigo Kimura and Lu Sun, Redirected Transfer Learning for Robust Multi-Layer Subspace Learning, Pattern Analysis and Applications, 27:25, 2024. DOI: 10.1007/s10044-024-01233-8
  
• Jiaqi Bao, Mineichi Kudo, Keigo Kimura and Lu Sun, “Robust embedding regression for semi-supervised learning”, Pattern Recognition, 145, 2024. DOI: 10.1016/j.patcog.2023.109894
  
• Lu Sun and Mineichi Kudo, “Multi-Label Classification by Polytree-Augmented Classifier Chains with Label-Dependent Features”, Pattern Analysis and Applications, 22(3), 1029-1049, 2019. DOI:10.1007/s10044-018-0711-6
  
• Lu Sun and Mineichi Kudo, “Optimization of Classifier Chains via Conditional Likelihood Maximization”, Pattern Recognition, 74: 503-517, 2018. DOI:10.1016/j.patcog.2017.09.034
  
• Lu Sun, Mineichi Kudo and Keigo Kimura, “READER: Robust Semi-Supervised Multi-Label Dimension Reduction”, IEICE Transactions on Information and Systems, E100-D(10): 2597-2604, 2017. DOI:10.1587/transinf.2017EDP7184
  
• Jipeng Huang, Lu Sun, Shuang Qiao and Jianing Sun, “Novel Fast License Plate Location Method Based on Hierarchical Strategy”, Journal of Jilin University, 45(2): 639-644, 2015. (in Chinese) DOI:10.13229/j.cnki.jdxbgxb201502045
  

Peer-Reviewed Conference Papers

• Ke Bian, Lu Sun and Dengji Zhao, “Learning Compact Neural Networks via Generalized Structured Sparsity”, in Proceedings of the 27th European Conference on Artificial Intelligence (ECAI 2024), accepted, 2024, Santiago de Compostela, Spain.
  
• Tianxiao Cao, Lu Sun, Canh Hao Nguyen and Hiroshi Mamitsuka, “Learning Low-Rank Tensor Cores with Probabilistic ℓ0-Regularized Rank Selection for Model Compression”, in Proceedings of the 33rd International Joint Conference on Artificial Intelligence (IJCAI 2024), accepted, 2024, Jeju, Korea.
  
• Jiahui Xu, Lu Sun and Dengji Zhao, “MoME: Mixture-of-Masked-Experts for Efficient Multi-Task Recommendation”, in Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 2024), 2527-2531, 2024, Washington D.C., USA.
  
• Weijia Lin, Jiankun Wang, Lu Sun, Mineichi Kudo and Keigo Kimura, “Multi-Label Personalized Classification via Exclusive Sparse Tensor Factorization”, in Proceedings of the 23rd IEEE International Conference on Data Mining (ICDM 2023), 398-407, 2023, Shanghai, China.
  
• Haruhi Mizuguchi, Keigo Kimura, Mineichi Kudo and Lu Sun, “Partial Multi-label Learning with a Few Accurately Labeled Data”, in Proceedings of the 20th Pacific Rim International Conference on Artificial Intelligence (PRICAI 2023), 79-90, 2023, Jakarta, Indonesia.
  
• Zhiwei Li, Zijian Yang, Lu Sun, Mineichi Kudo and Keigo Kimura, “Incomplete Multi-View Weak-Label Learning with Noisy Features and Imbalanced Labels”, in Proceedings of the 20th Pacific Rim International Conference on Artificial Intelligence (PRICAI 2023), 124-130, 2023, Jakarta, Indonesia.
  
• Luhuan Fei, Lu Sun, Mineichi Kudo and Keigo Kimura, “Structured Sparse Multi-Task Learning with Generalized Group Lasso”, in Proceedings of the 26th European Conference on Artificial Intelligence (ECAI 2023), 692-699, 2023, Krakow, Poland.
  
• Xinyi Wang, Lu Sun, Canh Hao Nguyen and Hiroshi Mamitsuka, “Multiplicative Sparse Tensor Factorization for Multi-View Multi-Task Learning”, in Proceedings of the 26th European Conference on Artificial Intelligence (ECAI 2023), 2560-2567, 2023, Krakow, Poland.
  
• Jiachun Jin, Jiankun Wang, Lu Sun, Jie Zheng and Mineichi Kudo, “Grouped Multi-Task Learning with Hidden Tasks Enhancement”, in Proceedings of the 26th European Conference on Artificial Intelligence (ECAI 2023), 1164-1171, 2023, Krakow, Poland.
  
• Zijian Yang, Zhiwei Li and Lu Sun, “Generalized Discriminative Deep Non-Negative Matrix Factorization Based on Latent Feature and Basis Learning”, in Proceedings of the 32nd International Joint Conference on Artificial Intelligence (IJCAI 2023), 4486-4494, 2023, Macao, China.
  
• Songjie Xie, Youlong Wu, Kewen Liao, Lu Chen, Chengfei Liu, Haifeng Shen, MingJian Tang, Lu Sun, “Fed-SC: One-Shot Federated Subspace Clustering over High-Dimensional Data”, in Proceedings of the 39th IEEE International Conference on Data Engineering (ICDE 2023), 2905-2918, 2023, Anaheim, California, USA.
  
• Jiankun Wang and Lu Sun, “Multi-Task Personalized Learning with Sparse Network Lasso”, in Proceedings of the 31st International Joint Conference on Artificial Intelligence (IJCAI 2022), 3516-3522, 2022, Vienna, Austria.
  
• Mineichi Kudo, Keigo Kimura, Shumpei Morishita and Lu Sun, “Efficient Leave-One-Out Evaluation of Kernelized Implicit Mappings”, in Proceedings of the joint IAPR International Workshops on Structural and Syntactic Pattern Recognition and Statistical Techniques in Pattern Recognition (S+SSPR 2022), 223-232, 2022, Montreal, Canada.
  
• Shumpei Morishita, Mineichi Kudo, Keigo Kimura and Lu Sun, “Realization of Autoencoders by Kernel Methods”, in Proceedings of the joint IAPR International Workshops on Structural and Syntactic Pattern Recognition and Statistical Techniques in Pattern Recognition (S+SSPR 2022), 1-10, 2022, Montreal, Canada.
  
• Keigo Kimura, Jiaqi Bao, Mineichi Kudo and Lu Sun, “Retargeted Regression Methods for Multi-Label Learning”, in Proceedings of the joint IAPR International Workshops on Structural and Syntactic Pattern Recognition and Statistical Techniques in Pattern Recognition (S+SSPR 2022), 203-212, 2022, Montreal, Canada.
  
• Lu Sun, Canh Hao Nguyen, Hiroshi Mamitsuka, “Fast and Robust Multi-View Multi-Task Learning via Group Sparsity”, in Proceedings of the 28th International Joint Conference on Artificial Intelligence (IJCAI 2019), 3499-3505, 2019, Macao, China.
  
• Lu Sun, Canh Hao Nguyen, Hiroshi Mamitsuka, “Multiplicative Sparse Feature Decomposition for Efficient Multi-View Multi-Task Learning”, in Proceedings of the 28th International Joint Conference on Artificial Intelligence (IJCAI 2019), 3506-3512, 2019, Macao, China.
  
• Lu Sun, Mineichi Kudo and Keigo Kimura, “Multi-Label Classification with Meta-Label-Specific Features”, in Proceedings of the 23rd International Conference on Pattern Recognition (ICPR 2016), 1612-1617, 2016, Cancun, Mexico.
  
• Keigo Kimura, Mineichi Kudo, Lu Sun and Sadamori Koujaku, “Fast Random k-labelsets for Large-Scale Multi-Label Classification”, in Proceedings of the 23rd International Conference on Pattern Recognition (ICPR 2016), 438-443, 2016, Cancun, Mexico.
  
• Batzaya Norov-Erdene, Mineichi Kudo, Lu Sun and Keigo Kimura, “Locality in Multi-Label Classification Problems”, in Proceedings of the 23rd International Conference on Pattern Recognition (ICPR 2016), 2319-2324, 2016, Cancun, Mexico.
  
• Keigo Kimura, Mineichi Kudo, Lu Sun, “Simultaneous Nonlinear Label-Instance Embedding for Multi-label Classification”, in Proceedings of the joint IAPR International Workshops on Structural and Syntactic Pattern Recognition and Statistical Techniques in Pattern Recognition (S+SSPR 2016), 15-25, 2016, Merida, Mexico.
  
• Lu Sun, Mineichi Kudo and Keigo Kimura, “A Scalable Clustering-Based Local Multi-Label Classification Method”, in Proceedings of the 22nd European Conference on Artificial Intelligence (ECAI 2016), 261-268, 2016, The Hague, Netherlands.
  
• Keigo Kimura, Mineichi Kudo and Lu Sun, “Dimension Reduction Using Nonnegative Matrix Tri-Factorization in Multi-label Classification”, in Proceedings of the 21st International Conference on Parallel & Distributed Processing Techniques & Applications: Workshop on Mathematical Modeling and Problem Solving (PDPTA 2015), 250-255, 2015, Las Vegas, USA.
  
• Lu Sun and Mineichi Kudo, “Polytree-Augmented Classifier Chains for Multi-Label Classification”, in Proceedings of the 24th International Joint Conference of Artificial Intelligence (IJCAI 2015), 3834-3840, 2015, Buenos Aires, Argentina.
  

Pre-Prints

• Keigo Kimura, Lu Sun and Mineichi Kudo, “MLC Toolbox: A MATLAB/OCTAVE Library for Multi-Label Classification”, CoRR abs/1704.02592, 2017. (preprinted in arXiv)