Gaussian weighted block sparse Bayesian learning strategy based on K-means clustering algorithm for accurate bioluminescence tomography in glioma

Author(s):  
Lin Yin ◽  
Kun Wang ◽  
Jie Tian
2016 ◽  
Vol 2016 ◽  
pp. 1-13
Author(s):  
Zhe Wang ◽  
Luyun Wang ◽  
Xiumei Li ◽  
Lifan Zhao ◽  
Guoan Bi

This paper describes a novel algorithm for underdetermined speech separation problem based on compressed sensing which is an emerging technique for efficient data reconstruction. The proposed algorithm consists of two steps. The unknown mixing matrix is firstly estimated from the speech mixtures in the transform domain by using K-means clustering algorithm. In the second step, the speech sources are recovered based on an autocalibration sparse Bayesian learning algorithm for speech signal. Numerical experiments including the comparison with other sparse representation approaches are provided to show the achieved performance improvement.


2016 ◽  
Vol E99.B (12) ◽  
pp. 2614-2622 ◽  
Author(s):  
Kai ZHANG ◽  
Hongyi YU ◽  
Yunpeng HU ◽  
Zhixiang SHEN ◽  
Siyu TAO

NeuroImage ◽  
2021 ◽  
pp. 118309
Author(s):  
Ali Hashemi ◽  
Chang Cai ◽  
Gitta Kutyniok ◽  
Klaus-Robert Müller ◽  
Srikantan S. Nagarajan ◽  
...  

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