scholarly journals Multi-task Joint Sparse Representation Classification Based on Fisher Discrimination Dictionary Learning

2018 ◽  
Vol 57 (1) ◽  
pp. 25-48 ◽  
Author(s):  
Rui Wang ◽  
Miaomiao Shen ◽  
Yanping Li ◽  
Samuel GOMES
Keyword(s):  
Sparse Representation ◽  
Dictionary Learning ◽  
Joint Sparse Representation ◽  
Sparse Representation Classification

Dictionary learning method for joint sparse representation-based image fusion

2013 ◽  
Vol 52 (5) ◽  
pp. 057006 ◽  
Author(s):  
Qiheng Zhang ◽  
Yuli Fu ◽  
Haifeng Li ◽  
Jian Zou
Keyword(s):  
Image Fusion ◽  
Sparse Representation ◽  
Dictionary Learning ◽  
Learning Method ◽  
Joint Sparse Representation

scholarly journals Multi-View Multi-Instance Learning Based on Joint Sparse Representation and Multi-View Dictionary Learning

2017 ◽  
Vol 39 (12) ◽  
pp. 2554-2560 ◽  
Author(s):  
Bing Li ◽  
Chunfeng Yuan ◽  
Weihua Xiong ◽  
Weiming Hu ◽  
Houwen Peng ◽  
...  
Keyword(s):  
Sparse Representation ◽  
Dictionary Learning ◽  
Joint Sparse Representation

scholarly journals AIRBORNE LIDAR POINTS CLASSIFICATION BASED ON TENSOR SPARSE REPRESENTATION

2017 ◽  
Vol IV-2/W4 ◽  
pp. 107-114
Author(s):  
N. Li ◽  
N. Pfeifer ◽  
C. Liu
Keyword(s):  
Sparse Representation ◽  
Statistical Methods ◽  
Dictionary Learning ◽  
Supervised Classification ◽  
Airborne Lidar ◽  
Training Data ◽  
Sparse Representation Classification ◽  
The Common

The common statistical methods for supervised classification usually require a large amount of training data to achieve reasonable results, which is time consuming and inefficient. This paper proposes a tensor sparse representation classification (SRC) method for airborne LiDAR points. The LiDAR points are represented as tensors to keep attributes in its spatial space. Then only a few of training data is used for dictionary learning, and the sparse tensor is calculated based on tensor OMP algorithm. The point label is determined by the minimal reconstruction residuals. Experiments are carried out on real LiDAR points whose result shows that objects can be distinguished by this algorithm successfully.

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