Batch Dictionary Learning with Augmented Orthogonal Matching Pursuit

2019 ◽  
pp. 439-443
Author(s):  
Ping He ◽  
Baichuan Fan ◽  
Xiaohua Xu ◽  
Yali Liang ◽  
Yuan Lou ◽  
...  
Keyword(s):  
Dictionary Learning ◽  
Matching Pursuit ◽  
Orthogonal Matching Pursuit

scholarly journals Endmember Learning with K-Means through SCD Model in Hyperspectral Scene Reconstructions

2019 ◽  
Vol 5 (11) ◽  
pp. 85 ◽  
Author(s):  
Ayan Chatterjee ◽  
Peter W. T. Yuen
Keyword(s):  
Dictionary Learning ◽  
Sparse Coding ◽  
Clustering Algorithm ◽  
Matching Pursuit ◽  
Orthogonal Matching Pursuit ◽  
Scene Reconstruction ◽  
Data Sets ◽  
Practical Applications ◽  
Input Dataset ◽  
Better Than

This paper proposes a simple yet effective method for improving the efficiency of sparse coding dictionary learning (DL) with an implication of enhancing the ultimate usefulness of compressive sensing (CS) technology for practical applications, such as in hyperspectral imaging (HSI) scene reconstruction. CS is the technique which allows sparse signals to be decomposed into a sparse representation “a” of a dictionary D u . The goodness of the learnt dictionary has direct impacts on the quality of the end results, e.g., in the HSI scene reconstructions. This paper proposes the construction of a concise and comprehensive dictionary by using the cluster centres of the input dataset, and then a greedy approach is adopted to learn all elements within this dictionary. The proposed method consists of an unsupervised clustering algorithm (K-Means), and it is then coupled with an advanced sparse coding dictionary (SCD) method such as the basis pursuit algorithm (orthogonal matching pursuit, OMP) for the dictionary learning. The effectiveness of the proposed K-Means Sparse Coding Dictionary (KMSCD) is illustrated through the reconstructions of several publicly available HSI scenes. The results have shown that the proposed KMSCD achieves ~40% greater accuracy, 5 times faster convergence and is twice as robust as that of the classic Spare Coding Dictionary (C-SCD) method that adopts random sampling of data for the dictionary learning. Over the five data sets that have been employed in this study, it is seen that the proposed KMSCD is capable of reconstructing these scenes with mean accuracies of approximately 20–500% better than all competing algorithms adopted in this work. Furthermore, the reconstruction efficiency of trace materials in the scene has been assessed: it is shown that the KMSCD is capable of recovering ~12% better than that of the C-SCD. These results suggest that the proposed DL using a simple clustering method for the construction of the dictionary has been shown to enhance the scene reconstruction substantially. When the proposed KMSCD is incorporated with the Fast non-negative orthogonal matching pursuit (FNNOMP) to constrain the maximum number of materials to coexist in a pixel to four, experiments have shown that it achieves approximately ten times better than that constrained by using the widely employed TMM algorithm. This may suggest that the proposed DL method using KMSCD and together with the FNNOMP will be more suitable to be the material allocation module of HSI scene simulators like the CameoSim package.

scholarly journals Simultaneous Seismic Sources Separation Based on Matrioshka Orthogonal Matching Pursuit, Application in Oil and Gas Exploration

2020 ◽  
Vol 58 (7) ◽  
pp. 4529-4546
Author(s):  
Ekaterina Shipilova ◽  
Michel Barret ◽  
Matthieu Bloch ◽  
Jean-Luc Boelle ◽  
Jean-Luc Collette
Keyword(s):  
Oil And Gas ◽  
Matching Pursuit ◽  
Orthogonal Matching Pursuit ◽  
Seismic Sources ◽  
Oil And Gas Exploration

Design and Generalization Analysis of Orthogonal Matching Pursuit Algorithms

2011 ◽  
Vol 57 (8) ◽  
pp. 5326-5341 ◽  
Author(s):  
Zakria Hussain ◽  
John Shawe-Taylor ◽  
David R. Hardoon ◽  
Charanpal Dhanjal
Keyword(s):  
Matching Pursuit ◽  
Orthogonal Matching Pursuit

scholarly journals Perturbation Analysis of Orthogonal Matching Pursuit

2013 ◽  
Vol 61 (2) ◽  
pp. 398-410 ◽  
Author(s):  
Jie Ding ◽  
Laming Chen ◽  
Yuantao Gu
Keyword(s):  
Perturbation Analysis ◽  
Matching Pursuit ◽  
Orthogonal Matching Pursuit
Sign in / Sign up

Export Citation Format

Share Document