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

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

Unsupervised Joint Subspace and Dictionary Learning for Enhanced Cross-Domain Person Re-Identification

2018 ◽  
Vol 12 (6) ◽  
pp. 1263-1275 ◽  
Author(s):  
Lei Qi ◽  
Jing Huo ◽  
Xiaocong Fan ◽  
Yinghuan Shi ◽  
Yang Gao
Keyword(s):  
Dictionary Learning ◽  
Cross Domain

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.

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

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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