基本信息
views: 6619
Career Trajectory
Bio
The Ye Lab has been conducting fundamental research in machine learning and data mining, developing computational methods for biomedical data analysis, and building informatics software. We have developed novel machine learning algorithms for feature extraction from high-dimensional data, sparse learning, multi-task learning, transfer learning, active learning, multi-label classification, and matrix completion. We have developed the SLEP (Sparse Learning with Efficient Projections) package, which includes implementations of large-scale sparse learning models, and the MALSAR (Multi-tAsk Learning via StructurAl Regularization) package, which includes implementations of state-of-the-art multi-task learning models. SLEP achieves state-of-the-art performance for many sparse learning models, and it has become one of the most popular sparse learning software packages. With close collaboration with researchers at the biomedical field, we have successfully applied these methods for analyzing biomedical data, including clinical image data and genotype data.
Research Interests
Papers共 631 篇Author StatisticsCo-AuthorSimilar Experts
By YearBy Citation主题筛选期刊级别筛选合作者筛选合作机构筛选
时间
引用量
主题
期刊级别
合作者
合作机构
Yong He,Pan Fang, Yongtao Shan,Yuanfei Pan, Yanhong Wei,Yichang Chen, Yihao Chen, Yi Liu,Zhenyu Zeng,Zhan Zhou,Feng Zhu,Edward C. Holmes,Jieping Ye,Jun Li,Yuelong Shu,Mang Shi,Zhaorong Li
biorxiv(2024)
CoRR (2024)
Cited0Views0EIBibtex
0
0
CoRR (2024)
Cited0Views0EIBibtex
0
0
CoRR (2024)
Cited0Views0EIBibtex
0
0
arXiv (Cornell University) (2024)
arxiv(2024)
Cited0Views0Bibtex
0
0
IEEE transactions on knowledge and data engineeringno. 5 (2024): 2213-2223
CoRR (2024)
Cited0Views0EIBibtex
0
0
CoRR (2024)
Cited0Views0EIBibtex
0
0
Load More
Author Statistics
#Papers: 632
#Citation: 41684
H-Index: 101
G-Index: 192
Sociability: 7
Diversity: 3
Activity: 346
Co-Author
Co-Institution
D-Core
- 合作者
- 学生
- 导师
Data Disclaimer
The page data are from open Internet sources, cooperative publishers and automatic analysis results through AI technology. We do not make any commitments and guarantees for the validity, accuracy, correctness, reliability, completeness and timeliness of the page data. If you have any questions, please contact us by email: report@aminer.cn