顏佐榕研究員學術專題演講(110/04/12)

  • 2021-04-08
  • 楊 文敏

國立政治大學統計學系
     
主講人:顏佐榕研究員 (中央研究院統計科學研究所)
   目:An Attention Algorithm for Solving Large Scale Structured L0-norm Penalty Estimation Problems
      間:民國110412 (星期一) 下午130 
      點:國立政治大學逸仙樓050101教室
      要:
          Technology advances have enabled researchers to collect large amounts of data with lots of covariates. Because of the high volume (large $n$) and high variety (large $p$) properties, model estimation with such big data has posed great challenges for statisticians. In this paper we focus on the algorithmic aspect of these challenges. We propose a numerical procedure for solving large scale regression estimation problems involving a structured $l_{0}$-norm penalty function. This numerical procedure blends the ideas of randomization, blockwise coordinate descent algorithms, and a closed form representation of the proximal operator of the structured $l_{0}$-norm penalty function. In particular, it adopts an "attention'' mechanism that exploits the iteration errors to build a sampling distribution for picking up regression coefficients for updates. Simulation study shows the proposed numerical procedure is competitive when comparing with other algorithms for sparse estimation in terms of runtime and statistical accuracy when both the sample size and the number of covariates become large (This is joint work with Yu-Min Yen).