期刊论文、会议论文

Hu, Xiaohua

英文

2017 IEEE INTERNATIONAL CONFERENCE ON BIG DATA (BIG DATA)

2639-1589

2017

3882-3887

IEEE International Conference on Big Data (IEEE Big Data)

IEEE International Conference on Big Data

DEC 11-14, 2017

Boston, MA

[Shen, Xianjun;Zhu, Xianchao;Jiang, Xingpeng;Gao, Li;He, Tingting;Hu, Xiaohua] Cent China Normal Univ, Sch Comp, Wuhan, Hubei, Peoples R China.^[Shen, Xianjun;Hu, Xiaohua] Drexel Univ, Coll Comp & Informat, Philadelphia, PA 19104 USA.

IEEE, IEEE Comp Soc, ELSEVIER, CISCO

Nie, JY Obradovic, Z Suzumura, T Ghosh, R Nambiar, R Wang, C Zang, H BaezaYates, R Hu, X Kepner, J Cuzzocrea, A Tang, J Toyoda, M

345 E 47TH ST, NEW YORK, NY 10017 USA

IEEE

978-1-5386-2715-0

10.1109/BigData.2017.8258393

National Key Research and Development Program of China [2017YFC0909502]; National Natural Science Foundation of China [61532008]; International Cooperation Project of Hubei Province [2014BHE0017]; Self-determined Research Funds of CCNU from the Colleges' Basic Research and Operation of MOE [CCNU17TS0003, CCNU16JYKX018]

本校为第一且通讯机构

Known as phenotypic overlapping, some disease-related symptoms share a common pathological and physiological mechanism. Researchers attempt to visualize the phenotypic relationships between different human diseases from the perspective of machine learning, but traditional visualization methods may be subject to fundamental limitations of metric spaces. Multiple maps t-SNE regularization method, a probabilistic method for visualizing data points in multiple low-dimensional spaces has been proposed to address the limitation. However, the convergence speed is low when apply on the scale dataset. ...