64 citations · 329 across the 26 of their papers we have counts for
6 papers · 1 filter
Guaranteed Sampling Flexibility for Low-tubal-rank Tensor Completion
Bowen Su, Juntao You, HanQin Cai +1
While Bernoulli sampling is extensively studied in tensor completion, t-CUR sampling approximates low-tubal-rank tensors via lateral and horizontal subtensors. However, both method…
Towards Constituting Mathematical Structures for Learning to Optimize
Jialin Liu, Xiaohan Chen, Zhangyang Wang +2
Learning to Optimize (L2O), a technique that utilizes machine learning to learn an optimization algorithm automatically from data, has gained arising attention in recent years. A g…
Non-convex approaches for low-rank tensor completion under tubal sampling
Zheng Tan, Longxiu Huang, HanQin Cai +1
Tensor completion is an important problem in modern data analysis. In this work, we investigate a specific sampling strategy, referred to as tubal sampling. We propose two novel no…
Matrix Completion with Cross-Concentrated Sampling: Bridging Uniform Sampling and CUR Sampling
HanQin Cai, Longxiu Huang, Pengyu Li +1
While uniform sampling has been widely studied in the matrix completion literature, CUR sampling approximates a low-rank matrix via row and column samples. Unfortunately, both samp…
Learned Robust PCA: A Scalable Deep Unfolding Approach for High-Dimensional Outlier Detection
HanQin Cai, Jialin Liu, Wotao Yin
Robust principal component analysis (RPCA) is a critical tool in modern machine learning, which detects outliers in the task of low-rank matrix reconstruction. In this paper, we pr…
Fast Robust Tensor Principal Component Analysis via Fiber CUR Decomposition
HanQin Cai, Zehan Chao, Longxiu Huang +1
We study the problem of tensor robust principal component analysis (TRPCA), which aims to separate an underlying low-multilinear-rank tensor and a sparse outlier tensor from their…