統計諮詢學術演講 主講人：Prof. Hua Zhou (Department of Biostatistics, University of California, Los Angeles)
國立陽明交通大學統計學研究所
統計諮詢學術演講
題 目：Recent Advances in MM Optimization Algorithms
主講人：Prof. Hua Zhou (Department of Biostatistics, University of California, Los Angeles)
時 間：114年12月19日(星期五) 下午14:20-15:10
地 點：綜合一館427室
摘 要
The majorization-minimization (MM) principle is an extremely general framework for deriving optimization algorithms. It includes the expectation-maximization (EM) algorithm, proximal gradient algorithm, concave-convex procedure, quadratic lower bound algorithm, and proximal distance algorithm as special cases. Besides numerous applications in statistics, optimization, and imaging, the MM principle finds wide applications in large-scale machine learning problems such as matrix completion, discriminant analysis, and nonnegative matrix factorizations. This talk presents some novel applications of the MM principle in the big data setting, including parallel block least squares, deweighting weighted least squares, large-scale variance component model, independent component analysis, and multi-level Monte Carlo.