科研成果

       交通数据分析与挖掘实验室围绕以上四个主攻方向,近年来承担了相当数量的国家及省部级重大科研项目,共主持973课题2项,973子课题6项,863项目8项,国家科技重大专项子课题4项,国家自然科学基金50余项,其中国家自然科学基金杰出青年基金1项,国家自然科学基金国际合作重大项目1项,国家自然科学基金重点项目1项,并承担大量铁道部、教育部、北京市、交通部、船舶总公司、民航信息集团等省部级项目和其它科研开发项目,近三年科研项目合同经费年均2500余万元以上。
       多年来,实验室通过这些科学研究取得了一批国内一流、国际先进的研究成果。在顶级国际期刊与国际会议上发表大量高水平专业学术论文共计600余篇,其中SCI检索200余篇,包括本领域顶级刊物,如IEEE Trans. on Pattern Analysis and Machine Intelligence、IEEE Trans. on Knowledge and Data Engineering、IEEE Trans. on Multimedia、IEEE Trans. on Image Processing,IEEE Trans. on Signal Processing、IEEE Trans. on Neural Networks、IEEE Trans. on Computers、IEEE Trans. on Systems, Man, and Cybernetics等。实验室在数据挖掘和数字媒体信息处理的研究已被国内外机器学习领域同行广泛认可。
       以高水平学术成果和行业应用实践成果为基础,交通数据分析与挖掘实验室还获得了多项国家级及省部级奖项,共获得国家科技进步一等奖1项,国家科技进步二等奖2项,北京市科学技术奖一等奖1项,教育部高等学校科学技术进步一等奖1项,高等学校自然科学奖二等奖1项,其他省部级科技进步一、二、三等奖13项,北京市科学技术协会北京青年优秀科技论文评选一等奖1项。
       在培养创新型科研人才方面,交通数据分析与挖掘实验室近年来成功培育出了教育部创新团队1支,国家杰出青年基金获得者1人,教育部跨(新)世纪优秀人才3人,享受国务院政府特殊津贴人员2人,北京市科技新星2人,铁道部中青年有突出贡献专家2人,入选国家百千万人才工程1人,铁道部青年科技拔尖人才1人,国家级教学名师1人。培养博士研究生100余名,硕士研究生200余名,其中获得全国百篇优秀博士论文提名1人,北京市优秀博士论文2人,中国计算机学会优秀博士论文1人。
       从2013年到2015年9月实验室共发表学术论文130多篇,其中SCI检索论文60余篇。具有代表性的论文包括IEEE/ACM Trans. 11篇,Nature Communication 2篇,Cell Research 1篇,顶级会议CVPR和IJCAI各一篇,及其他重要期刊论文近10篇。

       代表性的论文列表如下:

  1. Zhou, X., Menche, J., Barabási, A. L., & Sharma, A. (2014). Human symptoms–disease network. Nature communications, 5.(IF=10.7)
  2. Chen, W., Liu, Y., Zhu, S., Green, C. D., Wei, G., & Han, J. D. J. (2014). Improved nucleosome-positioning algorithm iNPS for accurate nucleosome positioning from sequencing data. Nature communications, 5.(IF=10.7)
  3. Qin, L., Zhu, C., Zhao, Y., Bai, H., & Tian, H. (2013). Generalized gradient vector flow for snakes: new observations, analysis, and improvement. IEEE Transactions on Circuits and Systems for Video Technology, 23(5), 883-897.(IF=2.615)
  4. Ma, L., Moisan, L., Yu, J., & Zeng, T. (2013). A dictionary learning approach for Poisson image deblurring. IEEE Transactions on Medical Imaging, 32(7), 1277-1289.(IF=3.39)
  5. Jing, L., & Ng, M. K. (2014). Sparse Label-Indicator Optimization Methods for Image Classification. IEEE Transactions on Image Processing, 23(3), 1002-1014.(IF=3.1)
  6. Feng, S., Feng, Z., & Jin, R. (2015). Learning to Rank Image Tags With Limited Training Examples. IEEE Transactions on Image Processing, 24(4), 1223-1234.(IF=3.1)
  7. Bai, C., Li, J., Lin, Z., Yu, J., & Chen, Y. W. (2015). Penrose Demosaicking. IEEE Transactions on Image Processing, 24(5), 1672-1684.(IF=3.1)
  8. Feng, S., Xiong, W., Li, B., Lang, C., & Huang, X. (2014). Hierarchical sparse representation based Multi-Instance Semi-Supervised Learning with application to image categorization. Signal Processing, 94, 595-607.(IF=2.2)
  9. Jing, L., Wang, P., & Yang, L. (2015, July). Sparse probabilistic matrix factorization by Llaplace distribution for collaborative filtering. In Proceedings of the 24th International Conference on Artificial Intelligence (IJCAI)(pp. 1771-1777).
  10. Jing, L., Yang, L., Yu, J., & Ng, M. K. (2015). Semi-supervised Low-Rank Mapping Learning for Multi-label Classification. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (pp. 1483-1491).
  11. L. Yang, Liping Jing, Jian Yu, and M. Ng, Learning transferred weights from co-occurrence data for heterogeneous transfer learning, Neural Network and Learning Systems, IEEE Trans. on, Accepted, DOI:10.1109/TNNLS.2015.2472457, 2015. (IF=4.291)
  12. L. Yang, Liping Jing, and M. Ng, Robust and non-negative collective matrix factorization for text-to-image transfer learning, IEEE Trans. On Image Processing, Accepted, DOI: 10.1109/TIP.2015.2465157, 2015. (IF=3.625)
  13. Jing, L., Tian, K., & Huang, J. Z. (2015). Stratified Feature Sampling Method for Ensemble Clustering of High Dimensional Data. Pattern Recognition.(IF=3.096)
  14. Bai, C., Li, J., Lin, Z., Yu, J., & Chen, Y. W. (2015). Penrose Demosaicking. IEEE Transactions on Image Processing, 24(5), 1672-1684.(IF=3.625)
  15. Chaomurilige, Jian Yu, Mingshen Yang, Analysis of Parameter Selection for Gustafson-Kessel Fuzzy Clustering Using Jacobian Matrix, IEEE Trans. On Fuzzy Systems, issue 99, DIO: 10.1109/TFUZZ.2015.2421071, 2015 (IF=8.746)
  16. Lin, Youfang., Wan, H., Jiang, R., Wu, Z., & Jia, X. (2015). Inferring the Travel Purposes of Passenger Groups for Better Understanding of Passengers. IEEE Transactions on Intelligent Transportation Systems, 16(1), 235-243.(IF=2.472)
  17. Wan, H., Moens, M. F., Luyten, W., Zhou, X., Mei, Q., Liu, L., & Tang, J. (2015). Extracting relations from traditional chinese medicine literature via heterogeneous entity networks. Journal of the American Medical Informatics Association, ocv092.(IF=3.932)
  18. Wu, Zhihao., Menichetti, G., Rahmede, C., & Bianconi, G. (2015). Emergent Complex Network Geometry. Scientific Reports, 5.(IF=5.578)
  19. Qi Zou, Haibin Ling, Siwei Luo, Yaping Huang, Mei Tian, “Robust Nighttime Vehicle Detection by Tracking and Grouping Headlights", IEEE Transaction on Intelligent Transportation Systems, 16(5): 2838 – 2849, (2015) (IF=2.48)(点击查看论文内容)
  20. Wang, G., Liu, Q., He, R., Gao, F., & Tellambura, C. Acquisition of Channel State Information in Heterogeneous Cloud Radio Access Networks: Challenges and Research Directions. IEEE Wireless Communications (IF=5.417)
  21. Li, Q., Zhang, Z., Lu, W., Yang, J., Ma, Y., & Yao, W. (2015). From pixels to patches: a cloud classification method based on bag of micro-structures. Atmospheric Measurement Techniques Discussions, 8(10), 10213-10247.(IF=2.929)
  22. Li, J. E., Liu, Y., Liu, M., & Han, J. D. J. (2013). Functional dissection of regulatory models using gene expression data of deletion mutants. PLoS genetics, 9(9).(IF=7.528)
  23. Liu, Y., Qiao, N., Zhu, S., Su, M., Sun, N., Boyd-Kirkup, J., & Han, J. D. J. (2013). A novel Bayesian network inference algorithm for integrative analysis of heterogeneous deep sequencing data. Cell research, 23(3), 440.(IF=12.413)
  24. An, W., & Liang, M. (2013). Fuzzy support vector machine based on within-class scatter for classification problems with outliers or noises. Neurocomputing, 110, 101-110.(IF=2.083)
  25. Li, X., Zhou, X., Peng, Y., Liu, B., Zhang, R., Hu, J., ... & Sun, C. (2014). Network based integrated analysis of phenotype-genotype data for prioritization of candidate symptom genes. BioMed research international, 2014.(IF=2.7)