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 $\mathbf{\alpha }=\left({\alpha }_1,{\alpha }_2\right)$. 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.