scholarly journals Analysis K-SVD: A Dictionary-Learning Algorithm for the Analysis Sparse Model

2013 ◽  
Vol 61 (3) ◽  
pp. 661-677 ◽  
Author(s):  
Ron Rubinstein ◽  
Tomer Peleg ◽  
Michael Elad
Keyword(s):  
Dictionary Learning ◽  
Learning Algorithm ◽  
Sparse Model

scholarly journals Nonlinear Kernel Dictionary Learning Algorithm Based on Analysis Sparse Model

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 212456-212466
Author(s):  
Zhuoyun Miao ◽  
Hongjuan Zhang ◽  
Shuang Ma
Keyword(s):  
Dictionary Learning ◽  
Learning Algorithm ◽  
Sparse Model

scholarly journals Analysis Sparse Representation for Nonnegative Signals Based on Determinant Measure by DC Programming

Complexity ◽  
2018 ◽  
Vol 2018 ◽  
pp. 1-12 ◽  
Author(s):  
Yujie Li ◽  
Benying Tan ◽  
Atsunori Kanemura ◽  
Shuxue Ding ◽  
Wuhui Chen
Keyword(s):  
Sparse Representation ◽  
Dictionary Learning ◽  
Optimization Problem ◽  
Learning Algorithm ◽  
State Of The Art ◽  
Optimization Methods ◽  
Dc Programming ◽  
Sparse Model ◽  
Alternative Approach ◽  
Difference Of Convex

Analysis sparse representation has recently emerged as an alternative approach to the synthesis sparse model. Most existing algorithms typically employ the l0-norm, which is generally NP-hard. Other existing algorithms employ the l1-norm to relax the l0-norm, which sometimes cannot promote adequate sparsity. Most of these existing algorithms focus on general signals and are not suitable for nonnegative signals. However, many signals are necessarily nonnegative such as spectral data. In this paper, we present a novel and efficient analysis dictionary learning algorithm for nonnegative signals with the determinant-type sparsity measure which is convex and differentiable. The analysis sparse representation can be cast in three subproblems, sparse coding, dictionary update, and signal update, because the determinant-type sparsity measure would result in a complex nonconvex optimization problem, which cannot be easily solved by standard convex optimization methods. Therefore, in the proposed algorithms, we use a difference of convex (DC) programming scheme for solving the nonconvex problem. According to our theoretical analysis and simulation study, the main advantage of the proposed algorithm is its greater dictionary learning efficiency, particularly compared with state-of-the-art algorithms. In addition, our proposed algorithm performs well in image denoising.

A fast DC-based dictionary learning algorithm with the SCAD penalty

2021 ◽  
Vol 429 ◽  
pp. 89-100
Author(s):  
Zhenni Li ◽  
Chao Wan ◽  
Benying Tan ◽  
Zuyuan Yang ◽  
Shengli Xie
Keyword(s):  
Dictionary Learning ◽  
Learning Algorithm

Video Semantic Analysis

2021 ◽  
Vol 11 (2) ◽  
pp. 1-21
Author(s):  
Daniel Danso Essel ◽  
Ben-Bright Benuwa ◽  
Benjamin Ghansah
Keyword(s):  
Dictionary Learning ◽  
Semantic Analysis ◽  
Learning Algorithm ◽  
Recognition Rate ◽  
Image Data ◽  
Video Data ◽  
The Novel ◽  
Video Feature ◽  
Novel Approach ◽  
Video Semantic Analysis

Sparse Representation (SR) and Dictionary Learning (DL) based Classifier have shown promising results in classification tasks, with impressive recognition rate on image data. In Video Semantic Analysis (VSA) however, the local structure of video data contains significant discriminative information required for classification. To the best of our knowledge, this has not been fully explored by recent DL-based approaches. Further, similar coding findings are not being realized from video features with the same video category. Based on the foregoing, a novel learning algorithm, Sparsity based Locality-Sensitive Discriminative Dictionary Learning (SLSDDL) for VSA is proposed in this paper. In the proposed algorithm, a discriminant loss function for the category based on sparse coding of the sparse coefficients is introduced into structure of Locality-Sensitive Dictionary Learning (LSDL) algorithm. Finally, the sparse coefficients for the testing video feature sample are solved by the optimized method of SLSDDL and the classification result for video semantic is obtained by minimizing the error between the original and reconstructed samples. The experimental results show that, the proposed SLSDDL significantly improves the performance of video semantic detection compared with state-of-the-art approaches. The proposed approach also shows robustness to diverse video environments, proving the universality of the novel approach.

Projective Double Reconstructions Based Dictionary Learning Algorithm for Cross-Domain Recognition

2020 ◽  
Vol 29 ◽  
pp. 9220-9233
Author(s):  
Na Han ◽  
Jigang Wu ◽  
Xiaozhao Fang ◽  
Shaohua Teng ◽  
Guoxu Zhou ◽  
...  
Keyword(s):  
Dictionary Learning ◽  
Learning Algorithm ◽  
Cross Domain

An unsupervised dictionary learning algorithm for neural recordings

2015 ◽  
Author(s):  
Tao Xiong ◽  
Jie Zhang ◽  
Yuanming Suo ◽  
Dung N. Tran ◽  
Ralph Etienne-Cummings ◽  
...  
Keyword(s):  
Dictionary Learning ◽  
Learning Algorithm ◽  
Neural Recordings
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