Low Rank Tensor Recovery via Cubic Sketching
- 11:30AM at LWSN B134
- Prof. Guang Cheng, Purdue University
- Low Rank Tensor Recovery via Cubic Sketching
In this presentation, we propose a general framework for recovering sparse and low-rank tensors through rank-one cubic sketching. Two real-world applications are considered: one on high-dimensional interaction models; another on compressed image transmission. A block-wise thresholded gradient decent algorithm is proposed for stable recovery in both noiseless and noisy cases. Both upper bound and lower bounds for the estimation accuracy are obtained over a large class of low-rank tensors, demonstrating the optimality of the proposed procedure. This is an ongoing work with Botao Hao and Anru Zhang.