Resolution:
## Figure 1.
Workflow of this study. Initially two similarity matrices of different views were used as input after standardization
to the z-value and renormalization. Then a two-step alternative minimization was used
to obtain the proper weights for the two similarity matrix in fusion. In the first
step, given the initial weights . cross-entropy between the input matrices and a combined non-negative factorization
was minimized by an EM algorithm. In the second step, given the calculated cross-entropy,
the weights were calculated by minimizing the object function, i.e. the cross-entropy
and entropy of the weight. The two steps iterate until convergence. The final α was
used as an ideal weighing vector that obtains balance between weighted sparseness
and informativeness.
Xu |