Low-rank modeling and its applications in image analysis
Low-rank modeling generally refers to a class of methods that solves problems by
representing variables of interest as low-rank matrices. It has achieved great success in …
representing variables of interest as low-rank matrices. It has achieved great success in …
Laplacian regularized low-rank representation and its applications
Low-rank representation (LRR) has recently attracted a great deal of attention due to its
pleasing efficacy in exploring low-dimensional subspace structures embedded in data. For a …
pleasing efficacy in exploring low-dimensional subspace structures embedded in data. For a …
Weighted low-rank tensor recovery for hyperspectral image restoration
Hyperspectral imaging, providing abundant spatial and spectral information simultaneously,
has attracted a lot of interest in recent years. Unfortunately, due to the hardware limitations …
has attracted a lot of interest in recent years. Unfortunately, due to the hardware limitations …
Learning structured low-rank representations for image classification
An approach to learn a structured low-rank representation for image classification is
presented. We use a supervised learning method to construct a discriminative and …
presented. We use a supervised learning method to construct a discriminative and …
Face recognition via collaborative representation: Its discriminant nature and superposed representation
W Deng, J Hu, J Guo - IEEE transactions on pattern analysis …, 2017 - ieeexplore.ieee.org
Collaborative representation methods, such as sparse subspace clustering (SSC) and
sparse representation-based classification (SRC), have achieved great success in face …
sparse representation-based classification (SRC), have achieved great success in face …
Low-rank multi-view embedding learning for micro-video popularity prediction
Recently, a prevailing trend of user generated content (UGC) on social media sites is the
emerging micro-videos. Microvideos afford many potential opportunities ranging from …
emerging micro-videos. Microvideos afford many potential opportunities ranging from …
In defense of sparsity based face recognition
W Deng, J Hu, J Guo - … of the IEEE conference on computer …, 2013 - openaccess.thecvf.com
The success of sparse representation based classification (SRC) has largely boosted the
research of sparsity based face recognition in recent years. A prevailing view is that the …
research of sparsity based face recognition in recent years. A prevailing view is that the …
Learning by associating ambiguously labeled images
We study in this paper the problem of learning classifiers from ambiguously labeled images.
For instance, in the collection of new images, each image contains some samples of interest …
For instance, in the collection of new images, each image contains some samples of interest …
Sparse and dense hybrid representation via dictionary decomposition for face recognition
X Jiang, J Lai - IEEE transactions on pattern analysis and …, 2014 - ieeexplore.ieee.org
Sparse representation provides an effective tool for classification under the conditions that
every class has sufficient representative training samples and the training data are …
every class has sufficient representative training samples and the training data are …