The initial error covariance matrices are set to diag(100,100,10,10) for all the nodes.
The weights of five MS similarity matrices are set as w i (i=1,…,5) using the CSF curve.
Therefore, in order to exploit this correlation, the generator matrices are set exactly the same for all sensors.
The initialization in Figure 4(c) of primal variables is set to be some circles, which leads that the cluster center coherency matrices are set unreasonable.
Compared to the result in Figure 4(d), the segmentation is smoother because the cluster center coherency matrices are set artificially in a supervised way.
The weights w i (i=1,…,5) of the five similarity matrices are set using the CSF to obtain a single similarity matrix S M. The GM similarity matrix S G of R and D is calculated.
The SNR-dependent smoothing parameters a and b in the adaptation of covariance matrices are set to and 0.9, respectively, to make the smoothing factor become 0 and 1 at the SNRs of 30 dB and dB, respectively.
First, the row-sums and column-sums of the two estimated matrices are set to be equal.
The values of the matrices were set to 1 if the expression of the gene in the given sample was higher than 2-fold compared to the other group and 0 if not.
The G LAj matrices were set up using the approach of Fernando and Grossman [ 12] based on the segregation probabilities, i.e. the probability of inheriting a paternally or maternally derived allele.
