A co-training algorithm for EEG classification with biomimetic pattern recognition and sparse representation

2014 ◽  
Vol 137 ◽  
pp. 212-222 ◽  
Author(s):  
Yuanfang Ren ◽  
Yan Wu ◽  
Yanbin Ge
2014 ◽  
Vol 687-691 ◽  
pp. 4123-4127 ◽  
Author(s):  
Jia Qing Miao

Recent years, the image sparse representation has been the popular method in the study of image representation, which has put forward a new idea in the image denoising. Its basic principle is that the original image has the sparse representation under the proper over-complete dictionary. Filter out the noise, we should find out the sparse representation of the image through the design of the dictionary. Its mechanism is that one hand the useful information of the image would be effectively expressed because of the sparse decomposition algorithm based on the redundant dictionary. The other the noise would not be expressed through the dictionary atoms. We do the image denoising according to the image sparse representation. Because of the superiority of the adaptive dictionary algorithm in the image, in this paper, we discuss the over-complete dictionary training algorithm. And we prove the effectiveness through the MATLAB.


2018 ◽  
Vol 16 (6) ◽  
Author(s):  
Guangchun Gao ◽  
Lina Shang ◽  
Kai Xiong ◽  
Jian Fang ◽  
Cui Zhang ◽  
...  

2004 ◽  
Vol 25 (2) ◽  
pp. 189-196 ◽  
Author(s):  
Ming-Jung Seow ◽  
Vijayan K. Asari

2013 ◽  
Vol 457-458 ◽  
pp. 1317-1322 ◽  
Author(s):  
Xiu Mei Guo ◽  
Lin Mei Wan ◽  
Cheng Yi Wang

As a recently proposed technique, sparse representation (SR) has been widely used for pattern recognition. Sparse representation emphasizes the coefficient sparsity and ignores the importance of the collaboration between classes. In this paper, collaborative representation is introduced for palmprint recognition, and the inter-class collaboration is employed to estimate the representation coefficient with regularized least square method. The algorithm based on collaborative representation was evaluated on the Hong Kong PolyU (v2) palmprint database and ideal recognition performance was achieved.


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