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

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 Simultaneous denoising of multicomponent microseismic data by joint sparse representation with dictionary learning

Geophysics ◽  
2019 ◽  
Vol 84 (5) ◽  
pp. KS155-KS172
Author(s):  
Jie Shao ◽  
Yibo Wang ◽  
Yi Yao ◽  
Shaojiang Wu ◽  
Qingfeng Xue ◽  
...  
Keyword(s):  
Sparse Representation ◽  
Dictionary Learning ◽  
Matching Pursuit ◽  
Signal To Noise Ratio ◽  
Random Noise ◽  
Time Consistency ◽  
Denoising Method ◽  
Microseismic Data ◽  
Joint Sparse Representation ◽  
Consistency Constraint

Microseismic data usually have a low signal-to-noise ratio, necessitating the application of an effective denoising method. Most conventional denoising methods treat each component of multicomponent data separately, e.g., denoising methods with sparse representation. However, microseismic data are often acquired with a 3C receiver, especially in borehole monitoring cases. Independent denoising ignores the relative amplitudes and vector relationships between different components. We have developed a new simultaneous denoising method for 3C microseismic data based on joint sparse representation. The three components are represented by different dictionary atoms; the dictionary can be fixed or adaptive depending on the dictionary learning method that is used. Our method adds an extra time consistency constraint with simultaneous transformation of 3C data. The joint sparse optimization problem is solved using the extended orthogonal matching pursuit. Synthetic microseismic data with a double-couple source mechanism and two field downhole microseismic data were used for testing. Independent denoising of 1C data with the fixed dictionary method and simultaneous denoising of 3C data with the fixed dictionary and dictionary learning (3C-DL) methods were compared. The results indicate that among the three methods, the 3C-DL method is the most effective in suppressing random noise, preserving weak signals, and restoring polarization information; this is achieved by combining the time consistency constraint and dictionary learning.

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