全校師生：
我校定于2020年04月22日舉辦研究生靈犀學術殿堂——夏志明教授報告會，現將有關事項通知如下：
1.報告會簡介
報告人：夏志明教授
時間：2020年04月22日（星期三）下午3：00（開始時間）
地點：騰訊會議，ID：561530041
報告題目：Deep PCA: A methodology of feature extraction and dimension reduction for high-order data
內容簡介：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.
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黨委學生工作部
數學與統計學院
2020年4月20日
報告人簡介
西北大學數學學院教授，博士生導師，西北大學現代統計研究中心副主任，主要致力于張量數據分析、大數據異質性結構推斷、分布式統計推斷與計算、生物統計學等數據科學理論與應用研究。在“Biometrika”、“Journal of machine learning research”,“Technometrics”、“Statistics in Medicine”、“Journal of Statistical Planning and Inference”、“Statistics”等國際統計與機器學習期刊以及“中國科學”、“應用概率統計”等國內期刊發表論文30余篇；主持國家自然科學基金項目3項，主持省部級項目3項,作為骨干成員獲得“陜西省科學技術進步獎”二、三等獎共2項，“陜西省高校科學技術獎”一等獎共2項，“陜西省國防科技進步獎”一等獎1項；先后赴香港科技大學、佛羅里達大學等科研機構進行專業訪問與學術交流。
