Performs mosaic integrative non-negative matrix factorization (UINMF) (A.R.
Kriebel, 2022) to return factorized \(H\), \(W\), \(V\) and \(U\)
matrices. The objective function is stated as
$$\arg\min_{H\ge0,W\ge0,V\ge0,U\ge0}\sum_{i}^{d}
||\begin{bmatrix}E_i \\ P_i \end{bmatrix} -
(\begin{bmatrix}W \\ 0 \end{bmatrix}+
\begin{bmatrix}V_i \\ U_i \end{bmatrix})Hi||^2_F+
\lambda_i\sum_{i}^{d}||\begin{bmatrix}V_i \\ U_i \end{bmatrix}H_i||_F^2$$
where \(E_i\) is the input non-negative matrix of the \(i\)'th dataset,
\(P_i\) is the input non-negative matrix for the unshared features,
\(d\) is the total number of datasets. \(E_i\) is of size
\(m \times n_i\) for \(m\) shared features and \(n_i\) sample points,
\(P_i\) is of size \(u_i \times n_i\) for \(u_i\) unshared feaetures,
\(H_i\) is of size \(k \times n_i\), \(V_i\) is of size
\(m \times k\), \(W\) is of size \(m \times k\) and \(U_i\) is of
size \(u_i \times k\).
Similar to inmf, uinmf also optimizes the objective with
ANLS algorithm.