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稀疏贝叶斯

于 2021-03-01 发布文件大小:479KB

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下载积分: 1 下载次数: 48

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代码说明：

基于稀疏贝叶斯学习的稀疏向量恢复算法，里面包含多个情景下的仿真程序和说明(Sparse vectors recovery based on SBL)

文件列表：

稀疏贝叶斯, 0 , 2011-11-13
稀疏贝叶斯\demo.m, 7923 , 2011-11-12
稀疏贝叶斯\demo_fig3.m, 3458 , 2011-11-12
稀疏贝叶斯\demo_fig6_SNR10.m, 2421 , 2011-11-12
稀疏贝叶斯\demo_fig8.m, 3360 , 2011-11-12
稀疏贝叶斯\demo_identicalVector.m, 5657 , 2011-11-12
稀疏贝叶斯\demo_time_varying.m, 7551 , 2011-11-12
稀疏贝叶斯\Master the Usage of TMSBL in 3 Minutes.docx, 95186 , 2011-11-13
稀疏贝叶斯\Master the Usage of TMSBL in 3 Minutes.pdf, 298066 , 2011-11-13
稀疏贝叶斯\Master the Usage of TSBL in 1 Minute.pdf, 101863 , 2011-11-12
稀疏贝叶斯\MFOCUSS.m, 5758 , 2011-11-12
稀疏贝叶斯\MSBL.m, 6214 , 2011-11-12
稀疏贝叶斯\perfSupp.m, 1912 , 2011-11-12
稀疏贝叶斯\ReadMe.txt, 1687 , 2011-11-12
稀疏贝叶斯\TMSBL.m, 18245 , 2011-11-13
稀疏贝叶斯\TSBL.m, 13981 , 2011-11-12

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kl
说明：（1）应用9×9的窗口对上述图象进行随机抽样，共抽样200块子图象；（2）将所有子图象按列相接变成一个81维的行向量；（3）对所有200个行向量进行KL变换，求出其对应的协方差矩阵的特征向量和特征值，按降序排列特征值以及所对应的特征向量；（4）选择前40个最大特征值所对应的特征向量作为主元，将原图象块向这40个特征向量上投影，所获得的投影系数就是这个子块的特征向量。（5）求出所有子块的特征向量。 ((1) the application of 9 × 9 window of these images at random, a total sample of 200 sub-image (2) all sub-images according to out-phase into a 81-dimensional row vector (3) all 200 lines for KL transform vector, derived its corresponding covariance matrix of eigenvectors and eigenvalues, in descending order by eigenvalue and the corresponding eigenvector (4) a choice to 40 corresponding to the largest eigenvalue eigenvector as the PCA, the original image block to the 40 feature vectors on the projection, the projection coefficients obtained by this sub-block eigenvector. (5) calculated for all sub-block eigenvector.)

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