Alg. 2: Use Bayesian method to find the dictionary D of similar block group Input: Similar block group $${\overline{Y}}_n,n=1,2,..N$$, K Gaussian distribution {N(μk, ∑k)}K = 1, 2, …, k through GMM leaning. Output: Gaussian component of similar block group $${\overline{Y}}_n$$ corresponded dictionary D. Step1. initialization n = 1,k = 1. Step2. Apply the formula $$\ln P\left(k\left|\overline{Y}\right.=\right)\sum \limits_{m=1}^M\ln N\left({y}_m\left|\overline{0},{\sum}_k\right.\right)-\ln C$$ to calculate $$\ln P\left(k\left|\overline{Y}\right.\right)$$ when taking the k-th Gaussian component. Step3. Repeat step 2, total of K times for calculating $$\ln P\left(k\left|\overline{Y}\right.\right)$$ values. Step4. Compare $$\ln P\left(k\left|\overline{Y}\right.\right),k=1,2,\dots, k$$, get the maximum $$\ln P\left(k\left|\overline{Y}\right.\right)$$, its corresponding Gaussian distribution can describe similar block group Yn, its covariance matrix is ∑k. Step5. For SVD decomposition, get dictionary Dn of similar block group Yn. Step6. Repeat steps 2–5, a total of N times, until the output N is a dictionary D.