Symmetric input matrix \(X\) of size \(n \times n\) is required. Two
approaches are provided. Alternating Non-negative Least Squares Block
Principal Pivoting algorithm (ANLSBPP) with symmetric regularization, where
the objective function is set to be \(\arg\min_{H\ge0,W\ge0}||X-WH||_F^2+
\lambda||W-H||_F^2\), can be run with algo = "anlsbpp".
Gaussian-Newton algorithm, where the objective function is set to be
\(\arg\min_{H\ge0}||X-H^\mathsf{T}H||_F^2\), can be run with algo =
"gnsym". In the objectives, \(W\) is of size \(n \times k\) and \(H\)
is of size \(k \times n\). The returned results will all be
\(n \times k\).
symNMF(
x,
k,
niter = 30L,
lambda = 0,
algo = "gnsym",
nCores = 2L,
Hinit = NULL
)A list with the following elements:
W - the result left-hand factor matrix, non-empty when using
"anlsbpp"
H - the result right hand matrix.
objErr - the objective error of the factorization.
Input matrix for factorization. Must be symmetric. Can be either dense or sparse.
Integer. Factor matrix rank.
Integer. Maximum number of symNMF interations.
Default 30
Symmetric regularization parameter. Must be
non-negative. Default 0.0 uses the square of the maximum value in
x.
Algorithm to perform the factorization, choose from "gnsym" or
"anlsbpp". Default "gnsym"
The number of parallel tasks that will be spawned. Only applies to anlsbpp.
Default 2
Initial right-hand factor matrix, must be of size n x k.
Default NULL.
Srinivas Eswar and et al., Distributed-Memory Parallel Symmetric Nonnegative Matrix Factorization, SC '20, 2020, 10.5555/3433701.3433799