全校師生：
我校定于2020年7月1日舉辦研究生靈犀學(xué)術(shù)殿堂——王建軍報(bào)告會(huì)，現(xiàn)將有關(guān)事項(xiàng)通知如下：
1.報(bào)告會(huì)簡(jiǎn)介
報(bào)告人：王建軍教授
時(shí)間：2020年7月1日（星期三） 15：00
地點(diǎn)：騰訊會(huì)議（會(huì)議號(hào)：274947281）
報(bào)告題目：Low-tubal-rank Tensor Analysis: Theory, Algorithms and Applications
內(nèi)容簡(jiǎn)介：This talk will share our two recent results on low-tubal-rank tensor analysis. (1) LRTR: we establish a regularized tensor nuclear norm minimization (RTNNM) model for low-tubal-rank tensor recovery (LRTR). Then, we initiatively define a novel tensor restricted isometry property (t-RIP) based on tensor singular value decomposition (t-SVD). Besides, our theoretical results show that any third-order tensor X∈R^(n_1×n_2×n_3 ) whose tubal rank is at most can stably be recovered from its as few as measurements with a bounded noise constraint via the RTNNM model, if the linear map obeys t-RIP .(2) TRPCA: by incorporating prior information including the column and row space knowledge, we investigate the tensor robust principal component analysis (TRPCA) problem based on t-SVD. We establish sufficient conditions to ensure that under significantly weaker incoherence assumptions than tensor principal components pursuit method (TPCP), our proposed Modified-TPCP solution perfectly recovers the low-tubal-rank and the sparse components with high probability, provided that the available prior subspace information is accurate. In addition, we present an efficient algorithm by modifying the alternating direction method of multipliers (ADMM) to solve the Modified-TPCP program. Numerical experiments show that the Modified-TPCP based on prior subspace information does allow us to recover under weaker conditions than TPCP. The application of color video and face denoising task suggests the superiority of the proposed method over the existing state-of-the-art methods.
2.歡迎各學(xué)院師生前來(lái)聽(tīng)報(bào)告。報(bào)告會(huì)期間請(qǐng)關(guān)閉手機(jī)或?qū)⑹謾C(jī)調(diào)至靜音模式。
黨委學(xué)生工作部
數(shù)學(xué)與統(tǒng)計(jì)學(xué)院
2020年6月24日
報(bào)告人簡(jiǎn)介
王建軍，博士，西南大學(xué)教授（研究員），博士生導(dǎo)師，重慶市創(chuàng)新創(chuàng)業(yè)領(lǐng)軍人才，巴渝學(xué)者特聘教授，重慶市學(xué)術(shù)帶頭人，美國(guó)數(shù)學(xué)評(píng)論評(píng)論員，重慶數(shù)學(xué)會(huì)理事，重慶市統(tǒng)計(jì)學(xué)重點(diǎn)學(xué)科學(xué)術(shù)帶頭人。主要研究方向?yàn)椋焊呔S數(shù)據(jù)建模、機(jī)器學(xué)習(xí)（深度學(xué)習(xí)）、數(shù)據(jù)挖掘、壓縮感知、張量數(shù)據(jù)建模、函數(shù)逼近論等。在神經(jīng)網(wǎng)絡(luò)復(fù)雜性和高維數(shù)據(jù)稀疏建模等方面有一定的學(xué)術(shù)積累。主持并完成國(guó)家自然科學(xué)基金4項(xiàng)（其中面上項(xiàng)目2項(xiàng)，青年項(xiàng)目2項(xiàng)），教育部科學(xué)技術(shù)重點(diǎn)項(xiàng)目1項(xiàng)，重慶市自然科學(xué)基金1項(xiàng)，主研5項(xiàng)國(guó)家自然、社會(huì)科學(xué)基金；現(xiàn)主持國(guó)家自然科學(xué)基金面上項(xiàng)目一項(xiàng)，參與國(guó)家重點(diǎn)基礎(chǔ)研究發(fā)展‘973’計(jì)劃一項(xiàng)，多次出席國(guó)際、國(guó)內(nèi)重要學(xué)術(shù)會(huì)議，并做特邀報(bào)告20余次。已在IEEE Transactions on Pattern Analysis and Machine Intelligence、Applied and Computational Harmonic Analysis , Inverse Problems，Neural Networks, Signal Processing, IEEE Signal Processing letters, Journal of Computational and Applied Mathematics, Neurocomputing, IET Signal Processing, IET Communication,中國(guó)科學(xué)(A,F輯),數(shù)學(xué)學(xué)報(bào),計(jì)算機(jī)學(xué)報(bào),電子學(xué)報(bào)，數(shù)學(xué)年刊等專(zhuān)業(yè)期刊發(fā)表90余篇學(xué)術(shù)論文?！吨袊?guó)科學(xué)》(A,F輯), IEEE Trans. Signal Process, image Process. Neural Networks and learning system及IEEE等系列刊物，Signal Processing，Neural Networks，Pattern Recognization，中國(guó)科學(xué)（A,F），計(jì)算機(jī)學(xué)報(bào)，電子學(xué)報(bào)，數(shù)學(xué)學(xué)報(bào)等知名期刊審稿人。2018年，以第一完成人申報(bào)的階段性成果《復(fù)雜結(jié)構(gòu)性高維數(shù)據(jù)稀疏建模的方法與算法應(yīng)用》榮獲重慶市自然科學(xué)三等獎(jiǎng)。
