NMF Algorithm - Sparse NMF via Alternating NNLS
NMF algorithms proposed by Kim et al. (2007) that enforces sparsity constraint on the basis matrix (algorithm ‘SNMF/L’) or the mixture coefficient matrix (algorithm ‘SNMF/R’).
nmfAlgorithm.SNMF_R(..., maxIter = 20000L, eta = -1,
beta = 0.01, bi_conv = c(0, 10), eps_conv = 1e-04)
nmfAlgorithm.SNMF_L(..., maxIter = 20000L, eta = -1,
beta = 0.01, bi_conv = c(0, 10), eps_conv = 1e-04)maxIter |
maximum number of iterations. |
eta |
parameter to suppress/bound the L2-norm of
If |
beta |
regularisation parameter for sparsity
control, which balances the trade-off between the
accuracy of the approximation and the sparseness of
Larger beta generates higher sparseness on |
bi_conv |
parameter of the biclustering convergence
test. It must be a size 2 numeric vector
Convergence checks are performed every 5 iterations. |
eps_conv |
threshold for the KKT convergence test. |
... |
extra argument not used. |
The algorithm ‘SNMF/R’ solves the following NMF optimization problem on a given target matrix A of dimension n x p:
min_{W,H} 1/2 (|| A - WH ||_F^2 + eta ||W||_F^2 + beta (sum_j ||H[,j]||_1^2)) s.t. W>=0, H>=0
The algorithm ‘SNMF/L’ solves a similar problem on
the transposed target matrix A, where H and
W swap roles, i.e. with sparsity constraints
applied to W.
Kim H and Park H (2007). "Sparse non-negative matrix factorizations via alternating non-negativity-constrained least squares for microarray data analysis." _Bioinformatics (Oxford, England)_, *23*(12), pp. 1495-502. ISSN 1460-2059, <URL: http://dx.doi.org/10.1093/bioinformatics/btm134>, <URL: http://www.ncbi.nlm.nih.gov/pubmed/17483501>.
Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.