Decomposition into low-rank plus additive matrices for background/foreground separation: A review for a comparative evaluation with a large-scale dataset
Background/foreground separation is the first step in video surveillance system to detect
moving objects. Recent research on problem formulations based on decomposition into low …
moving objects. Recent research on problem formulations based on decomposition into low …
Bilinear factor matrix norm minimization for robust PCA: Algorithms and applications
The heavy-tailed distributions of corrupted outliers and singular values of all channels in low-
level vision have proven effective priors for many applications such as background …
level vision have proven effective priors for many applications such as background …
[图书][B] Handbook of robust low-rank and sparse matrix decomposition: Applications in image and video processing
Handbook of Robust Low-Rank and Sparse Matrix Decomposition: Applications in Image
and Video Processing shows you how robust subspace learning and tracking by …
and Video Processing shows you how robust subspace learning and tracking by …
Trace norm regularized CANDECOMP/PARAFAC decomposition with missing data
In recent years, low-rank tensor completion (LRTC) problems have received a significant
amount of attention in computer vision, data mining, and signal processing. The existing …
amount of attention in computer vision, data mining, and signal processing. The existing …
LRR for Subspace Segmentation via Tractable Schatten- Norm Minimization and Factorization
Recently, nuclear norm-based low rank representation (LRR) methods have been popular in
several applications, such as subspace segmentation. However, there exist two limitations …
several applications, such as subspace segmentation. However, there exist two limitations …
Sparse matrix factorization with L2, 1 norm for matrix completion
Matrix factorization is a popular matrix completion method, however, it is difficult to
determine the ranks of the factor matrices. We propose two new sparse matrix factorization …
determine the ranks of the factor matrices. We propose two new sparse matrix factorization …
Matrix completion by least-square, low-rank, and sparse self-representations
Conventional matrix completion methods are generally based on rank minimization. These
methods assume that the given matrix is of low-rank and the data points are drawn from a …
methods assume that the given matrix is of low-rank and the data points are drawn from a …
Robust graph regularized nonnegative matrix factorization for clustering
Nonnegative matrix factorization and its graph regularized extensions have received
significant attention in machine learning and data mining. However, existing approaches are …
significant attention in machine learning and data mining. However, existing approaches are …
Survey on matrix completion models and algorithms
陈蕾， 陈松灿 - Journal of software, 2017 - jos.org.cn
近年来, 随着压缩感知技术在信号处理领域的巨大成功, 由其衍生而来的矩阵补全技术也日益
成为机器学习领域的研究热点, 诸多研究者针对矩阵补全问题展开了大量卓有成效的研究 …
成为机器学习领域的研究热点, 诸多研究者针对矩阵补全问题展开了大量卓有成效的研究 …