1 citations · 1 across the 7 of their papers we have counts for
7 papers
Separable Nonnegative Matrix Factorization Using Powered Ratio-of-Norms Regularization
Matthew McCarver, Jing Qin
Separable nonnegative matrix factorization (SNMF) has been widely used for low-rank representation and clustering of nonnegative data, owing to its ability to produce part-based an…
Robust Low-Tubal-Rank Tensor Completion under Cross-Concentrated Sampling
HanQin Cai, Longxiu Huang, Jing Qin +1
Tensor cross-concentrated sampling (t-CCS) bridges entrywise sampling and t-CUR slice-wise sampling by observing entries only within selected horizontal and lateral slices. Existin…
Time-Varying Graph Signal Recovery Using High-Order Smoothness and Adaptive Low-rankness
Weihong Guo, Yifei Lou, Jing Qin +1
Time-varying graph signal recovery has been widely used in many applications, including climate change, environmental hazard monitoring, and epidemic studies. It is crucial to choo…
Power of -Norm Regularized Kaczmarz Algorithms for High-Order Tensor Recovery
Katherine Henneberger, Jing Qin
Tensors serve as a crucial tool in the representation and analysis of complex, multi-dimensional data. As data volumes continue to expand, there is an increasing demand for develop…
Hyperspectral Band Selection based on Generalized 3DTV and Tensor CUR Decomposition
Katherine Henneberger, Jing Qin
Hyperspectral Imaging (HSI) serves as an important technique in remote sensing. However, high dimensionality and data volume typically pose significant computational challenges. Ba…
Log-Sum Regularized Kaczmarz Algorithms for High-Order Tensor Recovery
Katherine Henneberger, Jing Qin
Sparse and low rank tensor recovery has emerged as a significant area of research with applications in many fields such as computer vision. However, minimizing the -norm of…
Iterative Singular Tube Hard Thresholding Algorithms for Tensor Recovery
Rachel Grotheer, Shuang Li, Anna Ma +2
Due to the explosive growth of large-scale data sets, tensors have been a vital tool to analyze and process high-dimensional data. Different from the matrix case, tensor decomposit…