Fast Robust Matrix Completion via Entry-Wise ℓ0-Norm Minimization
Matrix completion (MC) aims at recovering missing entries, given an incomplete matrix.
Existing algorithms for MC are mainly designed for noiseless or Gaussian noise scenarios …
Existing algorithms for MC are mainly designed for noiseless or Gaussian noise scenarios …
Robust matrix completion for elliptic positioning in the presence of outliers and missing data
W ** Group Error Representation
W Lu, Z Fang, L Wu, L Tang, H Liu, C He - arxiv preprint arxiv:2407.08517, 2024 - arxiv.org
The low-rank matrix completion (LRMC) technology has achieved remarkable results in low-
level visual tasks. There is an underlying assumption that the real-world matrix data is low …
level visual tasks. There is an underlying assumption that the real-world matrix data is low …
Robust Low-Rank Matrix Completion via a New Sparsity-Inducing Regularizer
This paper presents a novel loss function referred to as hybrid ordinary-Welsch (HOW) and a
new sparsity-inducing regularizer associated with HOW. We theoretically show that the …
new sparsity-inducing regularizer associated with HOW. We theoretically show that the …
Semi-supervised learning with missing values imputation
Incomplete instances with various missing attributes in many real-world applications have
brought challenges to the classification tasks. Unsupervised imputation is often employed to …
brought challenges to the classification tasks. Unsupervised imputation is often employed to …