2021年中國自動化學會云講座正式起航！首期云講座將于3月29日15:00-16:00開講，本期云講座邀請到西南大學王建軍教授，為大家帶來報告：ROBUST ONE-BIT LOW-TUBAL-RANK TENSOR RECOVERY（1-比特低管秩張量魯棒恢復的模型、理論與算法），敬請期待！
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報告人：西南大學教授 王建軍
報告題目：
ROBUST ONE-BIT LOW-TUBAL-RANK TENSOR RECOVERY
1-比特低管秩張量魯棒恢復的模型、理論與算法
報告摘要：
This talk focuses on the recovery of low-tubal-rank tensors from binary measurements based on tensor-tensor product (or t-product) and tensor Singular Value Decomposition (t-SVD). Two types of recovery models are considered, one is the tensor hard singular tube thresholding and the other one is the tensor nuclear norm minimization. In the case no random dither exists in the measurements, our research shows that the direction of tensor X∈R^(n1×n2×n3) with tubal rank r can be well approximated from O(r(n1+n2)n3) random Gaussian measurements. In the case nonadaptive dither exists in the measurements, it is proved that both the direction and the magnitude of X can be simultaneously recovered. As we will see, under the nonadaptive measurement scheme, the recovery errors of two reconstruction procedures decay at the rate of polynomial of the oversampling factor λ=m/"r(n1+n2)n3" (m is the random Gaussian measurements). In order to obtain faster decay rate, we introduce a recursive strategy and allow the dithers in quantization to be adaptive to previous measurements for each iterations. Under this quantization scheme, two iterative recovery algorithms are proposed which establish recovery errors decaying at the rate of exponent of the oversampling factor λ. Numerical experiments on both synthetic and real-world data sets are conducted and demonstrate the validity of our theoretical results and the superiority of our algorithms.
報告人簡介：
王建軍，博士，西南大學三級教授，博士生導師，重慶市學術帶頭人，重慶市創新創業領軍人才，巴渝學者特聘教授，重慶工業與應用數學學會副理事長，CSIAM全國大數據與人工智能專家委員會委員，美國數學評論評論員，曾獲重慶市自然科學獎勵。主要研究方向為：高維數據建模、機器學習（深度學習）、數據挖掘、壓縮感知、張量分析、函數逼近論等。在神經網絡(深度學習)逼近復雜性和高維數據稀疏建模等方面有一定的學術積累。主持國家自然科學基金5項，教育部科學技術重點項目1項，重慶市自然科學基金1項，主研8項國家自然、社會科學基金；現主持國家自然科學基金面上項目2項，參與國家重點基礎研究發展‘973’計劃一項， 多次出席國際、國內重要學術會議，并應邀做大會特邀報告22余次。
已在IEEE Transactions on Pattern Analysis and Machine Intelligence（2）, IEEE Transactions on Neural Networks and Learning System（2），Applied and Computational Harmonic Analysis(2),Inverse Problems, Neural Networks, Signal Processing(2), IEEE Signal Processing letters(2), Journal of Computational and applied mathematics, ICASSP，IET Image processing(2), IET Signal processing(4),中國科學(A,F輯)(4), 數學學報, 計算機學報, 電子學報(3)等知名專業期刊發表90余篇學術論文,IEEE等系列刊物，National Science Review 及Signal Processing，Neural Networks，Pattern Recognization，中國科學, 計算機學報，電子學報，數學學報等知名期刊審稿人。
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來源：學會秘書處
