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 【学术报告】研究生“灵犀学术殿堂”第536期之夏志明教授报告会通知 2020-04-20 16:01   研究生院 审核人：   (点击： ) 全校师生： 我校定于2020年04月22日举办研究生灵犀学术殿堂——夏志明教授报告会，现将有关事项通知如下： 1.报告会简介 报告人：夏志明教授 时间：2020年04月22日（星期三）下午3：00（开始时间） 地点：腾讯会议，ID：561530041 报告题目：Deep PCA: A methodology of feature extraction and dimension reduction for high-order data 内容简介：Facing with rapidly-increasing demands for analyzing high-order data or multiway data, feature-extracting methods become imperative for analysis and processing. The traditional feature-extracting methods, however, either need to overly vectorize the data and smash the original structure hidden in data, such as PCA and PCA-like methods, which is unfavourable to the data recovery, or can not eliminate the redundant information very well, such as Tucker Decomposition (TD) and TD-like methods. To overcome these limitations, we propose a more flexible and more powerful tool, called the Deep Principal Components Analysis (Deep-PCA) in this paper. By segmenting a random tensor into equal-sized subarrays named \textit{sections} and maximizing variations caused by orthogonal projections of these \textit{sections}, the Deep-PCA finds principal components in a parsimonious and flexible way. In so doing, two new operations on tensors, the $S$-\textit{direction inner/outer product}, are introduced to formulate tensor projection and recovery. With different segmentation ways characterized by \textit{section depth} and \textit{direction}, the Deep-PCA can be implemented many times in different ways, which defines the sequential and global Deep-PCA respectively. These multiple Deep-PCA take the PCA and PCA-like, Tucker Decomposition and the TD-like as the special cases, which corresponds to the deepest section-depth and the shallowest section depth respectively. We propose an adaptive depth and direction selection algorithm for implementation of Deep-PCA. The Deep-PCA is then tested in terms of subspace recovery ability, compression ability and feature extraction performance when applied to a set of artificial data, surveillance videos and hyperspectral imaging data. All the tests support the flexibility, effectiveness and usefulness of Deep-PCA. 2.欢迎各学院师生前来听报告。报告会期间请关闭手机或将手机调至静音模式。 党委学生工作部 数学与统计学院 2020年4月20日 报告人简介 西北大学数学学院教授，博士生导师，西北大学现代统计研究中心副主任，主要致力于张量数据分析、大数据异质性结构推断、分布式统计推断与计算、生物统计学等数据科学理论与应用研究。在“Biometrika”、“Journal of machine learning research”,“Technometrics”、“Statistics in Medicine”、“Journal of Statistical Planning and Inference”、“Statistics”等国际统计与机器学习期刊以及“中国科学”、“应用概率统计”等国内期刊发表论文30余篇；主持国家自然科学基金项目3项，主持省部级项目3项,作为骨干成员获得“陕西省科学技术进步奖”二、三等奖共2项，“陕西省高校科学技术奖”一等奖共2项，“陕西省国防科技进步奖”一等奖1项；先后赴香港科技大学、佛罗里达大学等科研机构进行专业访问与学术交流。 【关闭窗口】