2017/05/17 by Zhen Li, Li, Zhen, Jingtian Bai +3
Computer Science · Engineering · Mathematics · #Bayesian Methods and Mixture Models #FOS: Computer and information sciences #Fault Detection and Control Systems #Machine Learning (stat.ML) #Statistical Methods and Inference
paper · pdf · doi:10.48550/arxiv.1705.06364
openalex publication_date 2017/05/17 · openalex created_date 2019/07/30 · openalex updated_date 2026/07/28
We develop a new method called Discriminated Hub Graphical Lasso (DHGL) based on Hub Graphical Lasso (HGL) by providing prior information of hubs. We apply this new method in two situations: with known hubs and without known hubs. Then we compare DHGL with HGL using several measures of performance. When some hubs are known, we can always estimate the precision matrix better via DHGL than HGL. When no hubs are known, we use Graphical Lasso (GL) to provide information of hubs and find that the performance of DHGL will always be better than HGL if correct prior information is given and will seldom degenerate when the prior information is wrong.