报告时间：2020 / 01 / 03（周五） 14:30-15:30
报告地点：明德主楼 1016 会议室
报告主题：Maximum Independent Component Analysis with Application to Brain EEG Data
报告摘要：In many scientific disciplines, finding hidden influential factors behind observational data is essential but challenging. The majority of existing approaches rely on linear transformation, i.e., true signals are linear combinations of hidden components. Motivated from analyzing non-linear temporal signals in neuroscience, genetics, and finance, this paper proposes the “maximum independent component analysis" (MaxICA), based on max-linear combinations of components. In contrast to existing methods, MaxICA benefits from focusing on significant major components while filtering out ignorable components. A major tool for parameter learning of MaxICA is an augmented genetic algorithm. Extensive empirical evaluations demonstrate the effectiveness of MaxICA in either extracting max-linearly combined essential sources in many applications or supplying a better approximation for non-linearly combined source signals, such as EEG recordings analyzed in this paper.
个人简介：张春明，现任美国威斯康星大学麦迪逊分校统计系教授。研究兴趣包括：高维复杂数据统计建模与推断，非参数与半参数统计建模与推断，大规模多元联合统计推断，及其应用于神经科学，脑科学研究及大脑成像数据分析，生物信息，医学，计量经济学及金融。