Federated topic discovery: A semantic consistent approach

• Main Authors: SHI, Yexuan, TONG, Yongxin, SU, Zhiyang, JIANG, Di, ZHOU, Zimu, ZHANG, Wenbin
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2020
• Subjects: Topic discovery; topic models; private data; Databases and Information Systems; Numerical Analysis and Scientific Computing
• Online Access: https://ink.library.smu.edu.sg/sis_research/6406; https://ink.library.smu.edu.sg/context/sis_research/article/7409/viewcontent/is21_shi_av.pdf
• Institution: Singapore Management University

General-purpose topic models have widespread industrial applications. Yet high-quality topic modeling is becoming increasingly challenging because accurate models require large amounts of training data typically owned by multiple parties, who are often unwilling to share their sensitive data for col...