A review on matrix completion for recommender systems
Recommender systems that predict the preference of users have attracted more and more
attention in decades. One of the most popular methods in this field is collaborative filtering …
attention in decades. One of the most popular methods in this field is collaborative filtering …
Graph moving object segmentation
JH Giraldo, S Javed… - IEEE Transactions on …, 2020 - ieeexplore.ieee.org
Moving Object Segmentation (MOS) is a fundamental task in computer vision. Due to
undesirable variations in the background scene, MOS becomes very challenging for static …
undesirable variations in the background scene, MOS becomes very challenging for static …
Low-rank quaternion approximation for color image processing
Low-rank matrix approximation (LRMA)-based methods have made a great success for
grayscale image processing. When handling color images, LRMA either restores each color …
grayscale image processing. When handling color images, LRMA either restores each color …
Computational drug repositioning based on multi-similarities bilinear matrix factorization
With the development of high-throughput technology and the accumulation of biomedical
data, the prior information of biological entity can be calculated from different aspects …
data, the prior information of biological entity can be calculated from different aspects …
Multiview subspace clustering via low-rank symmetric affinity graph
Multiview subspace clustering (MVSC) has been used to explore the internal structure of
multiview datasets by revealing unique information from different views. Most existing …
multiview datasets by revealing unique information from different views. Most existing …
Factor group-sparse regularization for efficient low-rank matrix recovery
This paper develops a new class of nonconvex regularizers for low-rank matrix recovery.
Many regularizers are motivated as convex relaxations of the\emph {matrix rank} function …
Many regularizers are motivated as convex relaxations of the\emph {matrix rank} function …
Efficient and effective nonconvex low-rank subspace clustering via SVT-free operators
With the growing interest in convex and nonconvex low-rank matrix learning problems, the
widely used singular value thresholding (SVT) operators associated with rank relaxation …
widely used singular value thresholding (SVT) operators associated with rank relaxation …
Affine subspace robust low-rank self-representation: from matrix to tensor
Low-rank self-representation based subspace learning has confirmed its great effectiveness
in a broad range of applications. Nevertheless, existing studies mainly focus on exploring …
in a broad range of applications. Nevertheless, existing studies mainly focus on exploring …
On-line anomaly detection with high accuracy
Traffic anomaly detection is critical for advanced Internet management. Existing detection
algorithms generally convert the high-dimensional data to a long vector, which compromises …
algorithms generally convert the high-dimensional data to a long vector, which compromises …