科学研究
学术报告
当前位置: 学院主页 > 科学研究 > 学术报告 > 正文

Low tubal rank tensor sensing and robust PCA from quantized measurements

发布时间:2022-03-23 作者: 浏览次数:
Speaker: 王建军 DateTime: 2022年3月25日(周五)上午9:30--10:30
Brief Introduction to Speaker:

 王建军,教授,西南大学

Place: 腾讯会议:841657597
Abstract:Low-rank tensor Sensing (LRTS) is a natural extension of low-rank matrix Sensing (LRMS) to high-dimensional arrays, which aims to reconstruct an underlying tensor X from incomplete linear measurements M(X). However, LRTS ignores the error caused by quantization, limiting its application when the quantization is low-level. In this work, we take into account the impact of extreme quantization and suppose the quantizer degrades into a comparator that only acquires the signs of M(X). We still hope to recover X from these binary measurements. Under the tensor Singular Value Decomposition (t-SVD) framework, two recovery methods are proposed---the first is a tensor hard singular tube thresholding method; the second is a constrained tensor nuclear norm minimization method. These methods can recover a real n1*n2*n3 tensor X with tubal rank r from m random Gaussian binary measurements with errors decaying at a polynomial speed of the oversampling factor lambda:=m/((n_1+n_2)n_3r). To improve t...